
@article{raffel_exploring_2020,
	title = {Exploring the limits of transfer learning with a unified text-to-text transformer},
	volume = {21},
	issn = {1532-4435},
	abstract = {Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pretraining objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.},
	number = {1},
	journal = {J. Mach. Learn. Res.},
	author = {Raffel, Colin and Shazeer, Noam and Roberts, Adam and Lee, Katherine and Narang, Sharan and Matena, Michael and Zhou, Yanqi and Li, Wei and Liu, Peter J.},
	month = jan,
	year = {2020},
	keywords = {attention based models, deep learning, multi-task learning, natural language processing, transfer learning},
}

@misc{touvron_llama_2023,
	title = {{LLaMA}: {Open} and {Efficient} {Foundation} {Language} {Models}},
	shorttitle = {{LLaMA}},
	url = {http://arxiv.org/abs/2302.13971},
	doi = {10.48550/arXiv.2302.13971},
	abstract = {We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.},
	urldate = {2025-12-18},
	publisher = {arXiv},
	author = {Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timothée and Rozière, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
	month = feb,
	year = {2023},
	note = {arXiv:2302.13971},
	keywords = {Computer Science - Computation and Language},
}

@article{johansson_trade-off_2011,
	title = {Trade-{Off} {Between} {Accuracy} and {Interpretability} for {Predictive} {In} {Silico} {Modeling}},
	volume = {3},
	issn = {1756-8919, 1756-8927},
	url = {https://www.tandfonline.com/doi/full/10.4155/fmc.11.23},
	doi = {10.4155/fmc.11.23},
	language = {en},
	number = {6},
	urldate = {2025-12-15},
	journal = {Future Medicinal Chemistry},
	author = {Johansson, Ulf and Sönströd, Cecilia and Norinder, Ulf and Boström, Henrik},
	month = apr,
	year = {2011},
	pages = {647--663},
}

@inproceedings{marzouk_tractability_2024,
	series = {{ICML}'24},
	title = {On the tractability of {SHAP} explanations under {Markovian} distributions},
	abstract = {Thanks to its solid theoretical foundation, the SHAP framework is arguably one the most widely utilized frameworks for local explainability of ML models. Despite its popularity, its exact computation is known to be very challenging, proven to be NP-Hard in various configurations. Recent works have unveiled positive complexity results regarding the computation of the SHAP score for specific model families, encompassing decision trees, random forests, and some classes of boolean circuits. Yet, all these positive results hinge on the assumption of feature independence, often simplistic in real-world scenarios. In this article, we investigate the computational complexity of the SHAP score by relaxing this assumption and introducing a Markovian perspective. We show that, under the Markovian assumption, computing the SHAP score for the class of Weighted automata, Disjoint DNFs and Decision Trees can be performed in polynomial time, offering a first positive complexity result for the problem of SHAP score computation that transcends the limitations of the feature independence assumption},
	booktitle = {Proceedings of the 41st {International} {Conference} on {Machine} {Learning}},
	publisher = {JMLR.org},
	author = {Marzouk, Reda and De La Higuera, Colin},
	year = {2024},
	note = {event-place: Vienna, Austria},
}

@inproceedings{kim_help_2023,
	address = {Hamburg Germany},
	title = {"{Help} {Me} {Help} the {AI}": {Understanding} {How} {Explainability} {Can} {Support} {Human}-{AI} {Interaction}},
	isbn = {9781450394215},
	shorttitle = {"{Help} {Me} {Help} the {AI}"},
	url = {https://dl.acm.org/doi/10.1145/3544548.3581001},
	doi = {10.1145/3544548.3581001},
	language = {en},
	urldate = {2025-12-11},
	booktitle = {Proceedings of the 2023 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {ACM},
	author = {Kim, Sunnie S. Y. and Watkins, Elizabeth Anne and Russakovsky, Olga and Fong, Ruth and Monroy-Hernández, Andrés},
	month = apr,
	year = {2023},
	pages = {1--17},
}

@inproceedings{kim_interpretability_2018,
	title = {Interpretability {Beyond} {Feature} {Attribution}: {Quantitative} {Testing} with {Concept} {Activation} {Vectors} ({TCAV})},
	shorttitle = {Interpretability {Beyond} {Feature} {Attribution}},
	url = {https://proceedings.mlr.press/v80/kim18d.html},
	abstract = {The interpretation of deep learning models is a challenge due to their size, complexity, and often opaque internal state. In addition, many systems, such as image classifiers, operate on low-level features rather than high-level concepts. To address these challenges, we introduce Concept Activation Vectors (CAVs), which provide an interpretation of a neural net’s internal state in terms of human-friendly concepts. The key idea is to view the high-dimensional internal state of a neural net as an aid, not an obstacle. We show how to use CAVs as part of a technique, Testing with CAVs (TCAV), that uses directional derivatives to quantify the degree to which a user-defined concept is important to a classification result–for example, how sensitive a prediction of “zebra” is to the presence of stripes. Using the domain of image classification as a testing ground, we describe how CAVs may be used to explore hypotheses and generate insights for a standard image classification network as well as a medical application.},
	language = {en},
	urldate = {2025-12-11},
	booktitle = {Proceedings of the 35th {International} {Conference} on {Machine} {Learning}},
	publisher = {PMLR},
	author = {Kim, Been and Wattenberg, Martin and Gilmer, Justin and Cai, Carrie and Wexler, James and Viegas, Fernanda and Sayres, Rory},
	month = jul,
	year = {2018},
	pages = {2668--2677},
}

@inproceedings{poursabzi-sangdeh_manipulating_2021,
	address = {Yokohama Japan},
	title = {Manipulating and {Measuring} {Model} {Interpretability}},
	isbn = {9781450380966},
	url = {https://dl.acm.org/doi/10.1145/3411764.3445315},
	doi = {10.1145/3411764.3445315},
	language = {en},
	urldate = {2025-12-11},
	booktitle = {Proceedings of the 2021 {CHI} {Conference} on {Human} {Factors} in {Computing} {Systems}},
	publisher = {ACM},
	author = {Poursabzi-Sangdeh, Forough and Goldstein, Daniel G and Hofman, Jake M and Wortman Vaughan, Jennifer Wortman and Wallach, Hanna},
	month = may,
	year = {2021},
	pages = {1--52},
}

@article{ciatto_symbolic_2024,
	title = {Symbolic {Knowledge} {Extraction} and {Injection} with {Sub}-symbolic {Predictors}: {A} {Systematic} {Literature} {Review}},
	volume = {56},
	issn = {0360-0300, 1557-7341},
	shorttitle = {Symbolic {Knowledge} {Extraction} and {Injection} with {Sub}-symbolic {Predictors}},
	url = {https://dl.acm.org/doi/10.1145/3645103},
	doi = {10.1145/3645103},
	abstract = {In this article, we focus on the opacity issue of sub-symbolic machine learning predictors by promoting two complementary activities— 
              symbolic knowledge extraction 
              (SKE) and 
              symbolic knowledge injection 
              (SKI)—from and into sub-symbolic predictors. We consider as symbolic any language being intelligible and interpretable for both humans and computers. Accordingly, we propose general meta-models for both SKE and SKI, along with two taxonomies for the classification of SKE and SKI methods. By adopting an explainable artificial intelligence (XAI) perspective, we highlight how such methods can be exploited to mitigate the aforementioned opacity issue. Our taxonomies are attained by surveying and classifying existing methods from the literature, following a systematic approach, and by generalising the results of previous surveys targeting specific sub-topics of either SKE or SKI alone. More precisely, we analyse 132 methods for SKE and 117 methods for SKI, and we categorise them according to their purpose, operation, expected input/output data and predictor types. For each method, we also indicate the presence/lack of runnable software implementations. Our work may be of interest for data scientists aiming at selecting the most adequate SKE/SKI method for their needs, and may also work as suggestions for researchers interested in filling the gaps of the current state-of-the-art as well as for developers willing to implement SKE/SKI-based technologies.},
	language = {en},
	number = {6},
	urldate = {2025-12-11},
	journal = {ACM Computing Surveys},
	author = {Ciatto, Giovanni and Sabbatini, Federico and Agiollo, Andrea and Magnini, Matteo and Omicini, Andrea},
	month = jun,
	year = {2024},
	pages = {1--35},
}

@article{bhuyan_neuro-symbolic_2024,
	title = {Neuro-symbolic artificial intelligence: a survey},
	volume = {36},
	issn = {1433-3058},
	shorttitle = {Neuro-symbolic artificial intelligence},
	url = {https://doi.org/10.1007/s00521-024-09960-z},
	doi = {10.1007/s00521-024-09960-z},
	abstract = {The goal of the growing discipline of neuro-symbolic artificial intelligence (AI) is to develop AI systems with more human-like reasoning capabilities by combining symbolic reasoning with connectionist learning. We survey the literature on neuro-symbolic AI during the last two decades, including books, monographs, review papers, contribution pieces, opinion articles, foundational workshops/talks, and related PhD theses. Four main features of neuro-symbolic AI are discussed, including representation, learning, reasoning, and decision-making. Finally, we discuss the many applications of neuro-symbolic AI, including question answering, robotics, computer vision, healthcare, and more. Scalability, explainability, and ethical considerations are also covered, as well as other difficulties and limits of neuro-symbolic AI. This study summarizes the current state of the art in neuro-symbolic artificial intelligence.},
	language = {en},
	number = {21},
	urldate = {2025-12-10},
	journal = {Neural Computing and Applications},
	author = {Bhuyan, Bikram Pratim and Ramdane-Cherif, Amar and Tomar, Ravi and Singh, T. P.},
	month = jul,
	year = {2024},
	keywords = {Artificial intelligence, Knowledge representation and reasoning, Machine learning, Neural networks, Neuro-symbolic artificial intelligence, Spatial-temporal data},
	pages = {12809--12844},
}

@misc{ferrer_comment_2024,
	title = {Comment fonctionnent les transformateurs : {Exploration} détaillée de l'architecture des transformateurs},
	url = {https://www.datacamp.com/fr/tutorial/how-transformers-work},
	urldate = {2024-10-04},
	author = {Ferrer, Josep},
	month = oct,
	year = {2024},
}

@incollection{neely_song_2022,
	title = {A {Song} of ({Dis})agreement: {Evaluating} the {Evaluation} of {Explainable} {Artificial} {Intelligence} in {Natural} {Language} {Processing}},
	shorttitle = {A {Song} of ({Dis})agreement},
	url = {https://ebooks.iospress.nl/doi/10.3233/FAIA220190},
	language = {en},
	urldate = {2025-12-10},
	booktitle = {{HHAI2022}: {Augmenting} {Human} {Intellect}},
	publisher = {IOS Press},
	author = {Neely, Michael and Schouten, Stefan F. and Bleeker, Maurits and Lucic, Ana},
	year = {2022},
	doi = {10.3233/FAIA220190},
	pages = {60--78},
}

@book{russell_intelligence_2021,
	address = {Paris},
	edition = {4e éd},
	title = {Intelligence artificielle: une approche moderne},
	isbn = {9782326002210},
	shorttitle = {Intelligence artificielle},
	language = {fre},
	publisher = {Pearson},
	author = {Russell, Stuart Jonathan and Norvig, Peter and Popineau, Fabrice and Miclet, Laurent and Cadet, Claire},
	year = {2021},
}

@inproceedings{kastner_relation_2021,
	title = {On the {Relation} of {Trust} and {Explainability}: {Why} to {Engineer} for {Trustworthiness}},
	shorttitle = {On the {Relation} of {Trust} and {Explainability}},
	url = {https://ieeexplore.ieee.org/document/9582305},
	doi = {10.1109/REW53955.2021.00031},
	abstract = {Recently, requirements for the explainability of software systems have gained prominence. One of the primary motivators for such requirements is that explainability is expected to facilitate stakeholders’ trust in a system. Although this seems intuitively appealing, recent psychological studies indicate that explanations do not necessarily facilitate trust. Thus, explainability requirements might not be suitable for promoting trust.One way to accommodate this finding is, we suggest, to focus on trustworthiness instead of trust. While these two may come apart, we ideally want both: a trustworthy system and the stakeholder’s trust. In this paper, we argue that even though trustworthiness does not automatically lead to trust, there are several reasons to engineer primarily for trustworthiness – and that a system’s explainability can crucially contribute to its trustworthiness.},
	urldate = {2025-12-08},
	booktitle = {2021 {IEEE} 29th {International} {Requirements} {Engineering} {Conference} {Workshops} ({REW})},
	author = {Kästner, Lena and Langer, Markus and Lazar, Veronika and Schomäcker, Astrid and Speith, Timo and Sterz, Sarah},
	month = sep,
	year = {2021},
	keywords = {Conferences, Explainability, NFR, Psychology, Requirements, Requirements engineering, Software systems, Stakeholders, Trust, Trustworthiness, XAI},
	pages = {169--175},
}

@inproceedings{deters_x_2024,
	address = {Uppsala Sweden},
	title = {The {X} {Factor}: {On} the {Relationship} between {User} {eXperience} and {eXplainability}},
	isbn = {9798400709661},
	shorttitle = {The {X} {Factor}},
	url = {https://dl.acm.org/doi/10.1145/3679318.3685352},
	doi = {10.1145/3679318.3685352},
	language = {en},
	urldate = {2025-12-08},
	booktitle = {Nordic {Conference} on {Human}-{Computer} {Interaction}},
	publisher = {ACM},
	author = {Deters, Hannah and Droste, Jakob and Hess, Anne and Klös, Verena and Schneider, Kurt and Speith, Timo and Vogelsang, Andreas},
	month = oct,
	year = {2024},
	pages = {1--12},
}

@article{lin_survey_2022,
	title = {A survey of transformers},
	volume = {3},
	issn = {2666-6510},
	url = {https://www.sciencedirect.com/science/article/pii/S2666651022000146},
	doi = {10.1016/j.aiopen.2022.10.001},
	abstract = {Transformers have achieved great success in many artificial intelligence fields, such as natural language processing, computer vision, and audio processing. Therefore, it is natural to attract lots of interest from academic and industry researchers. Up to the present, a great variety of Transformer variants (a.k.a. X-formers) have been proposed, however, a systematic and comprehensive literature review on these Transformer variants is still missing. In this survey, we provide a comprehensive review of various X-formers. We first briefly introduce the vanilla Transformer and then propose a new taxonomy of X-formers. Next, we introduce the various X-formers from three perspectives: architectural modification, pre-training, and applications. Finally, we outline some potential directions for future research.},
	urldate = {2025-12-08},
	journal = {AI Open},
	author = {Lin, Tianyang and Wang, Yuxin and Liu, Xiangyang and Qiu, Xipeng},
	month = jan,
	year = {2022},
	keywords = {Deep learning, Pre-trained models, Self-attention, Transformer},
	pages = {111--132},
}

@inproceedings{delobelle_robbert_2020,
	address = {Online},
	title = {{RobBERT}: a {Dutch} {RoBERTa}-based {Language} {Model}},
	shorttitle = {{RobBERT}},
	url = {https://www.aclweb.org/anthology/2020.findings-emnlp.292},
	doi = {10.18653/v1/2020.findings-emnlp.292},
	language = {en},
	urldate = {2025-12-08},
	booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {EMNLP} 2020},
	publisher = {Association for Computational Linguistics},
	author = {Delobelle, Pieter and Winters, Thomas and Berendt, Bettina},
	year = {2020},
	pages = {3255--3265},
}

@inproceedings{aftan_survey_2023,
	title = {A {Survey} on {BERT} and {Its} {Applications}},
	url = {https://ieeexplore.ieee.org/document/10092289/references#references},
	doi = {10.1109/LT58159.2023.10092289},
	abstract = {A recently developed language representation model named Bidirectional Encoder Representation from Transformers (BERT) is based on an advanced trained deep learning approach that has achieved excellent results in many complex tasks, the same as classification, Natural Language Processing (NLP), prediction, etc. This survey paper mainly adopts the summary of BERT, its multiple types, and its latest developments and applications in various computer science and engineering fields. Furthermore, it puts forward BERT's problems and attractive future research trends in a different area with multiple datasets. From the findings, overall, the BERT and their recent types have achieved more accurate, fast, and optimal results in solving most complex problems than typical Machine and Deep Learning methods.},
	urldate = {2025-12-08},
	booktitle = {2023 20th {Learning} and {Technology} {Conference} ({L}\&{T})},
	author = {Aftan, Sulaiman and Shah, Habib},
	month = jan,
	year = {2023},
	keywords = {BERT, Bit error rate, Computer science, Deep learning, Machine Learning, Natural Language Processing model, Predictive models, Text analysis, Text mining, Transformers, bidirectional encoder},
	pages = {161--166},
}

@misc{wahab_dibert_2021,
	title = {{DIBERT}: {Dependency} {Injected} {Bidirectional} {Encoder} {Representations} from {Transformers}},
	copyright = {https://creativecommons.org/licenses/by/4.0/},
	shorttitle = {{DIBERT}},
	url = {https://www.techrxiv.org/doi/full/10.36227/techrxiv.16444611.v2},
	doi = {10.36227/techrxiv.16444611.v2},
	abstract = {{\textless}div{\textgreater} 
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		{\textless}/p{\textgreater}{\textless}div{\textgreater} 
			{\textless}div{\textgreater} 
				{\textless}div{\textgreater} 
					{\textless}p{\textgreater}In this paper, we propose a new model named DIBERT 
which stands for Dependency Injected Bidirectional Encoder 
Representations from Transformers. DIBERT is a variation of 
the BERT and has an additional third objective called Parent 
Prediction (PP) apart from Masked Language Modeling (MLM) 
and Next Sentence Prediction (NSP). PP injects the syntactic 
structure of a dependency tree while pre-training the DIBERT 
which generates syntax-aware generic representations. We use 
the WikiText-103 benchmark dataset to pre-train both BERT- 
Base and DIBERT. After fine-tuning, we observe that DIBERT 
performs better than BERT-Base on various downstream tasks 
including Semantic Similarity, Natural Language Inference and 
Sentiment Analysis. {\textless}/p{\textgreater} 
				{\textless}/div{\textgreater} 
			{\textless}/div{\textgreater} 
		{\textless}/div{\textgreater} 
				{\textless}/div{\textgreater} 
			{\textless}/div{\textgreater} 
		{\textless}/div{\textgreater}},
	urldate = {2025-12-08},
	author = {Wahab, Abdul and Sifa, Rafet},
	month = oct,
	year = {2021},
}

@article{lee_biobert_2020,
	title = {{BioBERT}: a pre-trained biomedical language representation model for biomedical text mining},
	volume = {36},
	copyright = {http://creativecommons.org/licenses/by/4.0/},
	issn = {1367-4803, 1367-4811},
	shorttitle = {{BioBERT}},
	url = {https://academic.oup.com/bioinformatics/article/36/4/1234/5566506},
	doi = {10.1093/bioinformatics/btz682},
	abstract = {Abstract 
             
              Motivation 
              Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. 
             
             
              Results 
              We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62\% F1 score improvement), biomedical relation extraction (2.80\% F1 score improvement) and biomedical question answering (12.24\% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. 
             
             
              Availability and implementation 
              We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.},
	language = {en},
	number = {4},
	urldate = {2025-12-08},
	journal = {Bioinformatics},
	author = {Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
	editor = {Wren, Jonathan},
	month = feb,
	year = {2020},
	pages = {1234--1240},
}

@misc{sanh_distilbert_2020,
	title = {{DistilBERT}, a distilled version of {BERT}: smaller, faster, cheaper and lighter},
	shorttitle = {{DistilBERT}, a distilled version of {BERT}},
	url = {http://arxiv.org/abs/1910.01108},
	doi = {10.48550/arXiv.1910.01108},
	abstract = {As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a BERT model by 40\%, while retaining 97\% of its language understanding capabilities and being 60\% faster. To leverage the inductive biases learned by larger models during pre-training, we introduce a triple loss combining language modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device study.},
	urldate = {2025-12-08},
	publisher = {arXiv},
	author = {Sanh, Victor and Debut, Lysandre and Chaumond, Julien and Wolf, Thomas},
	month = mar,
	year = {2020},
	note = {arXiv:1910.01108},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{martin_camembert_2020,
	address = {Online},
	title = {{CamemBERT}: a {Tasty} {French} {Language} {Model}},
	shorttitle = {{CamemBERT}},
	url = {https://www.aclweb.org/anthology/2020.acl-main.645},
	doi = {10.18653/v1/2020.acl-main.645},
	language = {en},
	urldate = {2025-12-08},
	booktitle = {Proceedings of the 58th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}},
	publisher = {Association for Computational Linguistics},
	author = {Martin, Louis and Muller, Benjamin and Ortiz Suárez, Pedro Javier and Dupont, Yoann and Romary, Laurent and De La Clergerie, Eric and Seddah, Djamé and Sagot, Benoît},
	year = {2020},
	pages = {7203--7219},
}

@inproceedings{douka_juribert_2021,
	address = {Punta Cana, Dominican Republic},
	title = {{JuriBERT}: {A} {Masked}-{Language} {Model} {Adaptation} for {French} {Legal} {Text}},
	shorttitle = {{JuriBERT}},
	url = {https://aclanthology.org/2021.nllp-1.9/},
	doi = {10.18653/v1/2021.nllp-1.9},
	abstract = {Language models have proven to be very useful when adapted to specific domains. Nonetheless, little research has been done on the adaptation of domain-specific BERT models in the French language. In this paper, we focus on creating a language model adapted to French legal text with the goal of helping law professionals. We conclude that some specific tasks do not benefit from generic language models pre-trained on large amounts of data. We explore the use of smaller architectures in domain-specific sub-languages and their benefits for French legal text. We prove that domain-specific pre-trained models can perform better than their equivalent generalised ones in the legal domain. Finally, we release JuriBERT, a new set of BERT models adapted to the French legal domain.},
	urldate = {2025-12-08},
	booktitle = {Proceedings of the {Natural} {Legal} {Language} {Processing} {Workshop} 2021},
	publisher = {Association for Computational Linguistics},
	author = {Douka, Stella and Abdine, Hadi and Vazirgiannis, Michalis and El Hamdani, Rajaa and Restrepo Amariles, David},
	editor = {Aletras, Nikolaos and Androutsopoulos, Ion and Barrett, Leslie and Goanta, Catalina and Preotiuc-Pietro, Daniel},
	month = nov,
	year = {2021},
	pages = {95--101},
}

@inproceedings{segonne_jargon_2024,
	title = {Jargon: {A} {Suite} of {Language} {Models} and {Evaluation} {Tasks} for {French} {Specialized} {Domains}},
	shorttitle = {Jargon},
	url = {https://hal.science/hal-04535557},
	abstract = {Pretrained Language Models (PLMs) are the de facto backbone of most state-of-the-art NLP systems. In this paper, we introduce a family of domain-specific pretrained PLMs for French, focusing on three important domains: transcribed speech, medicine, and law. We use a transformer architecture based on eﬀicient methods (LinFormer) to maximise their utility, since these domains often involve processing long documents. We evaluate and compare our models to state-of-the-art models on a diverse set of tasks and datasets, some of which are introduced in this paper. We gather the datasets into a new French-language evaluation benchmark for these three domains. We also compare various training configurations: continued pretraining, pretraining from scratch, as well as single- and multi-domain pretraining. Extensive domain-specific experiments show that it is possible to attain competitive downstream performance even when pre-training with the approximative LinFormer attention mechanism. For full reproducibility, we release the models and pretraining data, as well as contributed datasets.},
	language = {en},
	urldate = {2025-12-08},
	author = {Segonne, Vincent and Mannion, Aidan and Canul, Laura Cristina Alonzo and Audibert, Alexandre and Liu, Xingyu and Macaire, Cécile and Pupier, Adrien and Zhou, Yongxin and Aguiar, Mathilde and Herron, Felix and Norré, Magali and Amini, Massih-Reza and Bouillon, Pierrette and Eshkol-Taravella, Iris and Esperança-Rodier, Emmanuelle and François, Thomas and Goeuriot, Lorraine and Goulian, Jérôme and Lafourcade, Mathieu and Lecouteux, Benjamin and Portet, François and Ringeval, Fabien and Vandeghinste, Vincent and Coavoux, Maximin and Dinarelli, Marco and Schwab, Didier},
	month = may,
	year = {2024},
	pages = {9463},
}

@misc{antoun_arabert_2021,
	title = {{AraBERT}: {Transformer}-based {Model} for {Arabic} {Language} {Understanding}},
	shorttitle = {{AraBERT}},
	url = {http://arxiv.org/abs/2003.00104},
	doi = {10.48550/arXiv.2003.00104},
	abstract = {The Arabic language is a morphologically rich language with relatively few resources and a less explored syntax compared to English. Given these limitations, Arabic Natural Language Processing (NLP) tasks like Sentiment Analysis (SA), Named Entity Recognition (NER), and Question Answering (QA), have proven to be very challenging to tackle. Recently, with the surge of transformers based models, language-specific BERT based models have proven to be very efficient at language understanding, provided they are pre-trained on a very large corpus. Such models were able to set new standards and achieve state-of-the-art results for most NLP tasks. In this paper, we pre-trained BERT specifically for the Arabic language in the pursuit of achieving the same success that BERT did for the English language. The performance of AraBERT is compared to multilingual BERT from Google and other state-of-the-art approaches. The results showed that the newly developed AraBERT achieved state-of-the-art performance on most tested Arabic NLP tasks. The pretrained araBERT models are publicly available on https://github.com/aub-mind/arabert hoping to encourage research and applications for Arabic NLP.},
	urldate = {2025-12-08},
	publisher = {arXiv},
	author = {Antoun, Wissam and Baly, Fady and Hajj, Hazem},
	month = mar,
	year = {2021},
	note = {arXiv:2003.00104},
	keywords = {Computer Science - Computation and Language},
}

@inproceedings{vig_multiscale_2019,
	address = {Florence, Italy},
	title = {A {Multiscale} {Visualization} of {Attention} in the {Transformer} {Model}},
	url = {https://aclanthology.org/P19-3007/},
	doi = {10.18653/v1/P19-3007},
	abstract = {The Transformer is a sequence model that forgoes traditional recurrent architectures in favor of a fully attention-based approach. Besides improving performance, an advantage of using attention is that it can also help to interpret a model by showing how the model assigns weight to different input elements. However, the multi-layer, multi-head attention mechanism in the Transformer model can be difficult to decipher. To make the model more accessible, we introduce an open-source tool that visualizes attention at multiple scales, each of which provides a unique perspective on the attention mechanism. We demonstrate the tool on BERT and OpenAI GPT-2 and present three example use cases: detecting model bias, locating relevant attention heads, and linking neurons to model behavior.},
	urldate = {2025-12-04},
	booktitle = {Proceedings of the 57th {Annual} {Meeting} of the {Association} for {Computational} {Linguistics}: {System} {Demonstrations}},
	publisher = {Association for Computational Linguistics},
	author = {Vig, Jesse},
	editor = {Costa-jussà, Marta R. and Alfonseca, Enrique},
	month = jul,
	year = {2019},
	pages = {37--42},
}

@article{kotipalli_role_2024,
	title = {The {Role} of {Attention} {Mechanisms} in {Enhancing} {Transparency} and {Interpretability} of {Neural} {Network} {Models} in {Explainable} {AI}},
	url = {https://digitalcommons.harrisburgu.edu/cgi/viewcontent.cgi?params=/context/dandt/article/1000/&path_info=Bhargav_Report_Final.pdf},
	abstract = {In the rapidly evolving field of artificial intelligence (AI), deep learning models' interpretability and reliability are severely hindered by their complexity and opacity. Enhancing the transparency and interpretability of AI systems for humans is the primary objective of the emerging field of explainable AI (XAI). The attention mechanisms at the heart of XAI's work are based on human cognitive processes. Neural networks can now dynamically focus on relevant parts of the input data thanks to these mechanisms, which enhances interpretability and performance. This report covers in-depth talks of attention mechanisms in neural networks within XAI, as well as an analysis of the theoretical foundations, architectural applications, and empirical evidence showing how well they work to improve model transparency. The report provides a comprehensive analysis of the role of attention mechanisms in AI models to address ethical concerns, comply with regulatory requirements, and foster a deeper understanding and trust in AI systems. The report contributes to the discussion about bringing AI closer to human values and cognitive processes so that its advancements are impactful and responsible by conducting a thorough analysis.},
	language = {en},
	author = {Kotipalli, Bhargav},
	month = apr,
	year = {2024},
}

@inproceedings{shrikumar_not_2017,
	address = {Sydney, Australia},
	title = {Not {Just} a {Black} {Box}: {Learning} {Important} {Features} {Through} {Propagating} {Activation} {Differences}},
	volume = {70},
	shorttitle = {Not {Just} a {Black} {Box}},
	url = {http://arxiv.org/abs/1605.01713},
	doi = {10.48550/arXiv.1605.01713},
	abstract = {Note: This paper describes an older version of DeepLIFT. See https://arxiv.org/abs/1704.02685 for the newer version. Original abstract follows: The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Learning Important FeaTures), an efficient and effective method for computing importance scores in a neural network. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. We apply DeepLIFT to models trained on natural images and genomic data, and show significant advantages over gradient-based methods.},
	urldate = {2025-12-04},
	publisher = {Proceedings of the 34th
International Conference on Machine Learning},
	author = {Shrikumar, Avanti and Greenside, Peyton and Shcherbina, Anna and Kundaje, Anshul},
	month = apr,
	year = {2017},
	note = {arXiv:1605.01713},
	keywords = {Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Computer Science - Neural and Evolutionary Computing},
	pages = {3145 -- 3153},
}

@inproceedings{selvaraju_grad-cam_2017,
	title = {Grad-{CAM}: {Visual} {Explanations} from {Deep} {Networks} via {Gradient}-{Based} {Localization}},
	shorttitle = {Grad-{CAM}},
	url = {https://ieeexplore.ieee.org/document/8237336},
	doi = {10.1109/ICCV.2017.74},
	abstract = {We propose a technique for producing `visual explanations' for decisions from a large class of Convolutional Neural Network (CNN)-based models, making them more transparent. Our approach - Gradient-weighted Class Activation Mapping (Grad-CAM), uses the gradients of any target concept (say logits for `dog' or even a caption), flowing into the final convolutional layer to produce a coarse localization map highlighting the important regions in the image for predicting the concept. Unlike previous approaches, Grad- CAM is applicable to a wide variety of CNN model-families: (1) CNNs with fully-connected layers (e.g. VGG), (2) CNNs used for structured outputs (e.g. captioning), (3) CNNs used in tasks with multi-modal inputs (e.g. visual question answering) or reinforcement learning, without architectural changes or re-training. We combine Grad-CAM with existing fine-grained visualizations to create a high-resolution class-discriminative visualization, Guided Grad-CAM, and apply it to image classification, image captioning, and visual question answering (VQA) models, including ResNet-based architectures. In the context of image classification models, our visualizations (a) lend insights into failure modes of these models (showing that seemingly unreasonable predictions have reasonable explanations), (b) outperform previous methods on the ILSVRC-15 weakly-supervised localization task, (c) are more faithful to the underlying model, and (d) help achieve model generalization by identifying dataset bias. For image captioning and VQA, our visualizations show even non-attention based models can localize inputs. Finally, we design and conduct human studies to measure if Grad-CAM explanations help users establish appropriate trust in predictions from deep networks and show that Grad-CAM helps untrained users successfully discern a `stronger' deep network from a `weaker' one even when both make identical predictions. Our code is available at https: //github.com/ramprs/grad-cam/ along with a demo on CloudCV [2] and video at youtu.be/COjUB9Izk6E.},
	urldate = {2025-12-04},
	booktitle = {2017 {IEEE} {International} {Conference} on {Computer} {Vision} ({ICCV})},
	author = {Selvaraju, Ramprasaath R. and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
	month = oct,
	year = {2017},
	note = {ISSN: 2380-7504},
	keywords = {Cats, Computer architecture, Dogs, Knowledge discovery, Visualization},
	pages = {618--626},
}

@inproceedings{radford_improving_2018,
	title = {Improving {Language} {Understanding} by {Generative} {Pre}-{Training}},
	url = {https://www.semanticscholar.org/paper/Improving-Language-Understanding-by-Generative-Radford-Narasimhan/cd18800a0fe0b668a1cc19f2ec95b5003d0a5035},
	abstract = {Natural language understanding comprises a wide range of diverse tasks such as textual entailment, question answering, semantic similarity assessment, and document classiﬁcation. Although large unlabeled text corpora are abundant, labeled data for learning these speciﬁc tasks is scarce, making it challenging for discriminatively trained models to perform adequately. We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative ﬁne-tuning on each speciﬁc task. In contrast to previous approaches, we make use of task-aware input transformations during ﬁne-tuning to achieve effective transfer while requiring minimal changes to the model architecture. We demonstrate the effectiveness of our approach on a wide range of benchmarks for natural language understanding. Our general task-agnostic model outperforms discriminatively trained models that use architectures speciﬁcally crafted for each task, signiﬁcantly improving upon the state of the art in 9 out of the 12 tasks studied. For instance, we achieve absolute improvements of 8.9\% on commonsense reasoning (Stories Cloze Test), 5.7\% on question answering (RACE), and 1.5\% on textual entailment (MultiNLI).},
	urldate = {2025-12-04},
	author = {Radford, Alec and Narasimhan, Karthik and Salimans, Tim and Sutskever, Ilya},
	year = {2018},
}

@inproceedings{kalouli_gkr_2018,
	address = {New Orleans, Louisiana},
	title = {{GKR}: the {Graphical} {Knowledge} {Representation} for semantic parsing},
	shorttitle = {{GKR}},
	url = {https://aclanthology.org/W18-1304/},
	doi = {10.18653/v1/W18-1304},
	abstract = {This paper describes the first version of an open-source semantic parser that creates graphical representations of sentences to be used for further semantic processing, e.g. for natural language inference, reasoning and semantic similarity. The Graphical Knowledge Representation which is output by the parser is inspired by the Abstract Knowledge Representation, which separates out conceptual and contextual levels of representation that deal respectively with the subject matter of a sentence and its existential commitments. Our representation is a layered graph with each sub-graph holding different kinds of information, including one sub-graph for concepts and one for contexts. Our first evaluation of the system shows an F-score of 85\% in accurately representing sentences as semantic graphs.},
	urldate = {2025-11-26},
	booktitle = {Proceedings of the {Workshop} on {Computational} {Semantics} beyond {Events} and {Roles}},
	publisher = {Association for Computational Linguistics},
	author = {Kalouli, Aikaterini-Lida and Crouch, Richard},
	editor = {Blanco, Eduardo and Morante, Roser},
	month = jun,
	year = {2018},
	pages = {27--37},
}

@misc{vaswani_attention_2017,
	title = {Attention {Is} {All} {You} {Need}},
	url = {http://arxiv.org/abs/1706.03762},
	doi = {10.48550/arXiv.1706.03762},
	abstract = {The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.},
	urldate = {2025-10-24},
	publisher = {arXiv},
	author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
	month = dec,
	year = {2017},
	note = {arXiv:1706.03762},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@book{netter_regards_2019,
	title = {Regards sur le nouveau droit des données personnelles},
	url = {https://hal.science/hal-02357967},
	abstract = {Just over 40 years after the entry into force in France of the "informatique et libertés" law, the law on personal data seems to have progressed dramatically. In 2018, the new European regulation (known as the " GDPR ") came into force. A law and then an ordinance adapted French domestic law accordingly. The first part of 2019 was marked by the rise of the sanctions imposed by the CNIL.

Some of the contributions collected in this book address cross-cutting issues related to the new regulation, such as its territorial scope, competition from the American model, the scope of the notion of data controller, the existence of post-mortem rights over the data, or the limits of the principle of transparency in the face of the opacity of predictive algorithms. Others focus on a particular sector, whether it is banking, health, insurance or data on public officials.},
	urldate = {2025-11-24},
	author = {Netter, Emmanuel and Ndior, Valère and Puyraimond, Jean-Ferdinand and Vergnolle, Suzanne},
	editor = {d'Amiens, Centre de droit privé et de sciences criminelles},
	month = nov,
	year = {2019},
	keywords = {algorithmes prédictifs, données de santé, données français des données personnelles, droit américain des données, droit au déréférencement, droit bancaire, droit des assurances, droit européen des données personnelles, extraterritorialité du droit, principe de transparence, souveraineté numérique, transparence de la vie publique},
}

@misc{lundberg_unified_2017,
	title = {A {Unified} {Approach} to {Interpreting} {Model} {Predictions}},
	url = {http://arxiv.org/abs/1705.07874},
	doi = {10.48550/arXiv.1705.07874},
	abstract = {Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.},
	urldate = {2025-11-19},
	publisher = {arXiv},
	author = {Lundberg, Scott and Lee, Su-In},
	month = nov,
	year = {2017},
	note = {arXiv:1705.07874},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{ribeiro_model-agnostic_2016,
	title = {Model-{Agnostic} {Interpretability} of {Machine} {Learning}},
	url = {http://arxiv.org/abs/1606.05386},
	doi = {10.48550/arXiv.1606.05386},
	abstract = {Understanding why machine learning models behave the way they do empowers both system designers and end-users in many ways: in model selection, feature engineering, in order to trust and act upon the predictions, and in more intuitive user interfaces. Thus, interpretability has become a vital concern in machine learning, and work in the area of interpretable models has found renewed interest. In some applications, such models are as accurate as non-interpretable ones, and thus are preferred for their transparency. Even when they are not accurate, they may still be preferred when interpretability is of paramount importance. However, restricting machine learning to interpretable models is often a severe limitation. In this paper we argue for explaining machine learning predictions using model-agnostic approaches. By treating the machine learning models as black-box functions, these approaches provide crucial flexibility in the choice of models, explanations, and representations, improving debugging, comparison, and interfaces for a variety of users and models. We also outline the main challenges for such methods, and review a recently-introduced model-agnostic explanation approach (LIME) that addresses these challenges.},
	urldate = {2025-11-19},
	publisher = {arXiv},
	author = {Ribeiro, Marco Tulio and Singh, Sameer and Guestrin, Carlos},
	month = jun,
	year = {2016},
	note = {arXiv:1606.05386},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
}

@article{rudin_stop_2019,
	title = {Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead},
	volume = {1},
	copyright = {2019 Springer Nature Limited},
	issn = {2522-5839},
	url = {https://www.nature.com/articles/s42256-019-0048-x},
	doi = {10.1038/s42256-019-0048-x},
	abstract = {Black box machine learning models are currently being used for high-stakes decision making throughout society, causing problems in healthcare, criminal justice and other domains. Some people hope that creating methods for explaining these black box models will alleviate some of the problems, but trying to explain black box models, rather than creating models that are interpretable in the first place, is likely to perpetuate bad practice and can potentially cause great harm to society. The way forward is to design models that are inherently interpretable. This Perspective clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare and computer vision.},
	language = {en},
	number = {5},
	urldate = {2025-11-14},
	journal = {Nature Machine Intelligence},
	author = {Rudin, Cynthia},
	month = may,
	year = {2019},
	keywords = {Computer science, Criminology, Science, Statistics, technology and society},
	pages = {206--215},
}

@article{montavon_explaining_2017,
	title = {Explaining nonlinear classification decisions with deep {Taylor} decomposition},
	volume = {65},
	issn = {0031-3203},
	url = {https://www.sciencedirect.com/science/article/pii/S0031320316303582},
	doi = {10.1016/j.patcog.2016.11.008},
	abstract = {Nonlinear methods such as Deep Neural Networks (DNNs) are the gold standard for various challenging machine learning problems such as image recognition. Although these methods perform impressively well, they have a significant disadvantage, the lack of transparency, limiting the interpretability of the solution and thus the scope of application in practice. Especially DNNs act as black boxes due to their multilayer nonlinear structure. In this paper we introduce a novel methodology for interpreting generic multilayer neural networks by decomposing the network classification decision into contributions of its input elements. Although our focus is on image classification, the method is applicable to a broad set of input data, learning tasks and network architectures. Our method called deep Taylor decomposition efficiently utilizes the structure of the network by backpropagating the explanations from the output to the input layer. We evaluate the proposed method empirically on the MNIST and ILSVRC data sets.},
	urldate = {2025-11-07},
	journal = {Pattern Recognition},
	author = {Montavon, Grégoire and Lapuschkin, Sebastian and Binder, Alexander and Samek, Wojciech and Müller, Klaus-Robert},
	month = may,
	year = {2017},
	keywords = {Deep neural networks, Heatmapping, Image recognition, Relevance propagation, Taylor decomposition},
	pages = {211--222},
}

@book{molnar_interpretable_2025,
	title = {Interpretable {Machine} {Learning}},
	url = {https://christophm.github.io/interpretable-ml-book/},
	urldate = {2025-09-11},
	author = {Molnar, Christoph},
	month = mar,
	year = {2025},
}

@misc{abnar_quantifying_2020,
	title = {Quantifying {Attention} {Flow} in {Transformers}},
	url = {http://arxiv.org/abs/2005.00928},
	doi = {10.48550/arXiv.2005.00928},
	abstract = {In the Transformer model, "self-attention" combines information from attended embeddings into the representation of the focal embedding in the next layer. Thus, across layers of the Transformer, information originating from different tokens gets increasingly mixed. This makes attention weights unreliable as explanations probes. In this paper, we consider the problem of quantifying this flow of information through self-attention. We propose two methods for approximating the attention to input tokens given attention weights, attention rollout and attention flow, as post hoc methods when we use attention weights as the relative relevance of the input tokens. We show that these methods give complementary views on the flow of information, and compared to raw attention, both yield higher correlations with importance scores of input tokens obtained using an ablation method and input gradients.},
	urldate = {2025-10-24},
	publisher = {arXiv},
	author = {Abnar, Samira and Zuidema, Willem},
	month = may,
	year = {2020},
	note = {arXiv:2005.00928},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@phdthesis{elguendouze_explainable_2024,
	type = {thesis},
	title = {Explainable {Artificial} {Intelligence} approaches for {Image} {Captioning}},
	url = {https://theses.fr/2024ORLE1003},
	abstract = {L'évolution rapide des modèles de sous-titrage d'images, impulsée par l'intégration de techniques d'apprentissage profond combinant les modalités image et texte, a conduit à des systèmes de plus en plus complexes. Cependant, ces modèles fonctionnent souvent comme des boîtes noires, incapables de fournir des explications transparentes de leurs décisions. Cette thèse aborde l'explicabilité des systèmes de sous-titrage d'images basés sur des architectures Encodeur-Attention-Décodeur, et ce à travers quatre aspects. Premièrement, elle explore le concept d'espace latent, s'éloignant ainsi des approches traditionnelles basées sur l'espace de représentation originel. Deuxièmement, elle présente la notion de caractère décisif, conduisant à la formulation d'une nouvelle définition pour le concept d'influence/décisivité des composants dans le contexte de sous-titrage d'images explicable, ainsi qu'une approche par perturbation pour la capture du caractère décisif. Le troisième aspect vise à élucider les facteurs influençant la qualité des explications, en mettant l'accent sur la portée des méthodes d'explication. En conséquence, des variantes basées sur l'espace latent de méthodes d'explication bien établies telles que LRP et LIME ont été développées, ainsi que la proposition d'une approche d'évaluation centrée sur l'espace latent, connue sous le nom d'Ablation Latente. Le quatrième aspect de ce travail consiste à examiner ce que nous appelons la saillance et la représentation de certains concepts visuels, tels que la quantité d'objets, à différents niveaux de l'architecture de sous-titrage.},
	language = {fr},
	urldate = {2025-10-24},
	school = {Orléans},
	author = {Elguendouze, Sofiane},
	month = nov,
	year = {2024},
}

@misc{lopardo_attention_2024,
	title = {Attention {Meets} {Post}-hoc {Interpretability}: {A} {Mathematical} {Perspective}},
	shorttitle = {Attention {Meets} {Post}-hoc {Interpretability}},
	url = {http://arxiv.org/abs/2402.03485},
	doi = {10.48550/arXiv.2402.03485},
	abstract = {Attention-based architectures, in particular transformers, are at the heart of a technological revolution. Interestingly, in addition to helping obtain state-of-the-art results on a wide range of applications, the attention mechanism intrinsically provides meaningful insights on the internal behavior of the model. Can these insights be used as explanations? Debate rages on. In this paper, we mathematically study a simple attention-based architecture and pinpoint the differences between post-hoc and attention-based explanations. We show that they provide quite different results, and that, despite their limitations, post-hoc methods are capable of capturing more useful insights than merely examining the attention weights.},
	urldate = {2025-10-24},
	publisher = {arXiv},
	author = {Lopardo, Gianluigi and Precioso, Frederic and Garreau, Damien},
	month = jun,
	year = {2024},
	note = {arXiv:2402.03485},
	keywords = {Computer Science - Computation and Language, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@inproceedings{sippy_data_2020,
	title = {Data {Staining}: {A} {Method} for {Comparing} {Faithfulness} of {Explainers}},
	url = {https://aiweb.cs.washington.edu/ai/pubs/sippy-icml20.pdf},
	author = {Sippy, Jacob D and Bansal, Gagan and Weld, Daniel},
	year = {2020},
}

@misc{poeta_concept-based_2023,
	title = {Concept-based {Explainable} {Artificial} {Intelligence}: {A} {Survey}},
	shorttitle = {Concept-based {Explainable} {Artificial} {Intelligence}},
	url = {http://arxiv.org/abs/2312.12936},
	doi = {10.48550/arXiv.2312.12936},
	abstract = {The field of explainable artificial intelligence emerged in response to the growing need for more transparent and reliable models. However, using raw features to provide explanations has been disputed in several works lately, advocating for more user-understandable explanations. To address this issue, a wide range of papers proposing Concept-based eXplainable Artificial Intelligence (C-XAI) methods have arisen in recent years. Nevertheless, a unified categorization and precise field definition are still missing. This paper fills the gap by offering a thorough review of C-XAI approaches. We define and identify different concepts and explanation types. We provide a taxonomy identifying nine categories and propose guidelines for selecting a suitable category based on the development context. Additionally, we report common evaluation strategies including metrics, human evaluations and dataset employed, aiming to assist the development of future methods. We believe this survey will serve researchers, practitioners, and domain experts in comprehending and advancing this innovative field.},
	urldate = {2025-10-24},
	publisher = {arXiv},
	author = {Poeta, Eleonora and Ciravegna, Gabriele and Pastor, Eliana and Cerquitelli, Tania and Baralis, Elena},
	month = dec,
	year = {2023},
	note = {arXiv:2312.12936},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction},
}

@misc{dosovitskiy_image_2021,
	title = {An {Image} is {Worth} 16x16 {Words}: {Transformers} for {Image} {Recognition} at {Scale}},
	shorttitle = {An {Image} is {Worth} 16x16 {Words}},
	url = {http://arxiv.org/abs/2010.11929},
	doi = {10.48550/arXiv.2010.11929},
	abstract = {While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to replace certain components of convolutional networks while keeping their overall structure in place. We show that this reliance on CNNs is not necessary and a pure transformer applied directly to sequences of image patches can perform very well on image classification tasks. When pre-trained on large amounts of data and transferred to multiple mid-sized or small image recognition benchmarks (ImageNet, CIFAR-100, VTAB, etc.), Vision Transformer (ViT) attains excellent results compared to state-of-the-art convolutional networks while requiring substantially fewer computational resources to train.},
	urldate = {2025-10-24},
	publisher = {arXiv},
	author = {Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
	month = jun,
	year = {2021},
	note = {arXiv:2010.11929},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning},
}

@misc{deyoung_eraser_2020,
	title = {{ERASER}: {A} {Benchmark} to {Evaluate} {Rationalized} {NLP} {Models}},
	shorttitle = {{ERASER}},
	url = {http://arxiv.org/abs/1911.03429},
	doi = {10.48550/arXiv.1911.03429},
	abstract = {State-of-the-art models in NLP are now predominantly based on deep neural networks that are opaque in terms of how they come to make predictions. This limitation has increased interest in designing more interpretable deep models for NLP that reveal the `reasoning' behind model outputs. But work in this direction has been conducted on different datasets and tasks with correspondingly unique aims and metrics; this makes it difficult to track progress. We propose the Evaluating Rationales And Simple English Reasoning (ERASER) benchmark to advance research on interpretable models in NLP. This benchmark comprises multiple datasets and tasks for which human annotations of "rationales" (supporting evidence) have been collected. We propose several metrics that aim to capture how well the rationales provided by models align with human rationales, and also how faithful these rationales are (i.e., the degree to which provided rationales influenced the corresponding predictions). Our hope is that releasing this benchmark facilitates progress on designing more interpretable NLP systems. The benchmark, code, and documentation are available at https://www.eraserbenchmark.com/},
	urldate = {2025-10-24},
	publisher = {arXiv},
	author = {DeYoung, Jay and Jain, Sarthak and Rajani, Nazneen Fatema and Lehman, Eric and Xiong, Caiming and Socher, Richard and Wallace, Byron C.},
	month = apr,
	year = {2020},
	note = {arXiv:1911.03429},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Machine Learning},
}

@misc{russakovsky_imagenet_2015,
	title = {{ImageNet} {Large} {Scale} {Visual} {Recognition} {Challenge}},
	url = {http://arxiv.org/abs/1409.0575},
	doi = {10.48550/arXiv.1409.0575},
	abstract = {The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.},
	urldate = {2025-10-24},
	publisher = {arXiv},
	author = {Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and Berg, Alexander C. and Fei-Fei, Li},
	month = jan,
	year = {2015},
	note = {arXiv:1409.0575},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@misc{devlin_bert_2019,
	title = {{BERT}: {Pre}-training of {Deep} {Bidirectional} {Transformers} for {Language} {Understanding}},
	shorttitle = {{BERT}},
	url = {http://arxiv.org/abs/1810.04805},
	doi = {10.48550/arXiv.1810.04805},
	abstract = {We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial task-specific architecture modifications. BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5\% (7.7\% point absolute improvement), MultiNLI accuracy to 86.7\% (4.6\% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).},
	urldate = {2025-10-24},
	publisher = {arXiv},
	author = {Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
	month = may,
	year = {2019},
	note = {arXiv:1810.04805},
	keywords = {Computer Science - Computation and Language},
}

@misc{chefer_transformer_2021,
	title = {Transformer {Interpretability} {Beyond} {Attention} {Visualization}},
	url = {http://arxiv.org/abs/2012.09838},
	doi = {10.48550/arXiv.2012.09838},
	abstract = {Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks. In order to visualize the parts of the image that led to a certain classification, existing methods either rely on the obtained attention maps or employ heuristic propagation along the attention graph. In this work, we propose a novel way to compute relevancy for Transformer networks. The method assigns local relevance based on the Deep Taylor Decomposition principle and then propagates these relevancy scores through the layers. This propagation involves attention layers and skip connections, which challenge existing methods. Our solution is based on a specific formulation that is shown to maintain the total relevancy across layers. We benchmark our method on very recent visual Transformer networks, as well as on a text classification problem, and demonstrate a clear advantage over the existing explainability methods.},
	urldate = {2025-10-24},
	publisher = {arXiv},
	author = {Chefer, Hila and Gur, Shir and Wolf, Lior},
	month = apr,
	year = {2021},
	note = {arXiv:2012.09838},
	keywords = {Computer Science - Computer Vision and Pattern Recognition},
}

@inproceedings{kalouli_xplainli_2020,
	address = {Barcelona, Spain (Online)},
	title = {{XplaiNLI}: {Explainable} {Natural} {Language} {Inference} through {Visual} {Analytics}},
	shorttitle = {{XplaiNLI}},
	url = {https://www.aclweb.org/anthology/2020.coling-demos.9},
	doi = {10.18653/v1/2020.coling-demos.9},
	language = {en},
	urldate = {2025-10-24},
	booktitle = {Proceedings of the 28th {International} {Conference} on {Computational} {Linguistics}: {System} {Demonstrations}},
	publisher = {International Committee on Computational Linguistics (ICCL)},
	author = {Kalouli, Aikaterini-Lida and Sevastjanova, Rita and De Paiva, Valeria and Crouch, Richard and El-Assady, Mennatallah},
	year = {2020},
	pages = {48--52},
}

@inproceedings{kalouli_hy-nli_2020,
	address = {Barcelona, Spain (Online)},
	title = {Hy-{NLI}: a {Hybrid} system for {Natural} {Language} {Inference}},
	shorttitle = {Hy-{NLI}},
	url = {https://www.aclweb.org/anthology/2020.coling-main.459},
	doi = {10.18653/v1/2020.coling-main.459},
	language = {en},
	urldate = {2025-10-24},
	booktitle = {Proceedings of the 28th {International} {Conference} on {Computational} {Linguistics}},
	publisher = {International Committee on Computational Linguistics},
	author = {Kalouli, Aikaterini-Lida and Crouch, Richard and De Paiva, Valeria},
	year = {2020},
	pages = {5235--5249},
}

@book{samek_explainable_2019,
	address = {Cham},
	series = {Lecture {Notes} in {Computer} {Science}},
	title = {Explainable {AI}: {Interpreting}, {Explaining} and {Visualizing} {Deep} {Learning}},
	volume = {11700},
	copyright = {http://www.springer.com/tdm},
	isbn = {9783030289539 9783030289546},
	shorttitle = {Explainable {AI}},
	url = {http://link.springer.com/10.1007/978-3-030-28954-6},
	language = {en},
	urldate = {2025-09-17},
	publisher = {Springer International Publishing},
	editor = {Samek, Wojciech and Montavon, Grégoire and Vedaldi, Andrea and Hansen, Lars Kai and Müller, Klaus-Robert},
	year = {2019},
	doi = {10.1007/978-3-030-28954-6},
}

@incollection{ancona_gradient-based_2019,
	address = {Cham},
	title = {Gradient-{Based} {Attribution} {Methods}},
	isbn = {9783030289546},
	url = {https://doi.org/10.1007/978-3-030-28954-6_9},
	abstract = {The problem of explaining complex machine learning models, including Deep Neural Networks, has gained increasing attention over the last few years. While several methods have been proposed to explain network predictions, the definition itself of explanation is still debated. Moreover, only a few attempts to compare explanation methods from a theoretical perspective has been done. In this chapter, we discuss the theoretical properties of several attribution methods and show how they share the same idea of using the gradient information as a descriptive factor for the functioning of a model. Finally, we discuss the strengths and limitations of these methods and compare them with available alternatives.},
	language = {en},
	urldate = {2025-09-17},
	booktitle = {Explainable {AI}: {Interpreting}, {Explaining} and {Visualizing} {Deep} {Learning}},
	publisher = {Springer International Publishing},
	author = {Ancona, Marco and Ceolini, Enea and Öztireli, Cengiz and Gross, Markus},
	editor = {Samek, Wojciech and Montavon, Grégoire and Vedaldi, Andrea and Hansen, Lars Kai and Müller, Klaus-Robert},
	year = {2019},
	doi = {10.1007/978-3-030-28954-6_9},
	keywords = {Attribution methods, Deep Neural Networks, Explainable artificial intelligence},
	pages = {169--191},
}

@incollection{montavon_layer-wise_2019,
	address = {Cham},
	title = {Layer-{Wise} {Relevance} {Propagation}: {An} {Overview}},
	isbn = {9783030289546},
	shorttitle = {Layer-{Wise} {Relevance} {Propagation}},
	url = {https://doi.org/10.1007/978-3-030-28954-6_10},
	abstract = {For a machine learning model to generalize well, one needs to ensure that its decisions are supported by meaningful patterns in the input data. A prerequisite is however for the model to be able to explain itself, e.g. by highlighting which input features it uses to support its prediction. Layer-wise Relevance Propagation (LRP) is a technique that brings such explainability and scales to potentially highly complex deep neural networks. It operates by propagating the prediction backward in the neural network, using a set of purposely designed propagation rules. In this chapter, we give a concise introduction to LRP with a discussion of (1) how to implement propagation rules easily and efficiently, (2) how the propagation procedure can be theoretically justified as a ‘deep Taylor decomposition’, (3) how to choose the propagation rules at each layer to deliver high explanation quality, and (4) how LRP can be extended to handle a variety of machine learning scenarios beyond deep neural networks.},
	language = {en},
	urldate = {2025-09-17},
	booktitle = {Explainable {AI}: {Interpreting}, {Explaining} and {Visualizing} {Deep} {Learning}},
	publisher = {Springer International Publishing},
	author = {Montavon, Grégoire and Binder, Alexander and Lapuschkin, Sebastian and Samek, Wojciech and Müller, Klaus-Robert},
	editor = {Samek, Wojciech and Montavon, Grégoire and Vedaldi, Andrea and Hansen, Lars Kai and Müller, Klaus-Robert},
	year = {2019},
	doi = {10.1007/978-3-030-28954-6_10},
	keywords = {Deep Neural Networks, Deep Taylor Decomposition, Explanations, Layer-wise Relevance Propagation},
	pages = {193--209},
}

@misc{huang_annotated_2022,
	title = {The {Annotated} {Transformer}},
	url = {https://nlp.seas.harvard.edu/annotated-transformer/},
	urldate = {2025-09-12},
	author = {Huang, Austin and Subramanian, Suraj and Sum, Jonathan and Almubarak, Khalid and Biderman, Stella},
	year = {2022},
}

@inproceedings{bell_its_2022,
	address = {Seoul Republic of Korea},
	title = {It’s {Just} {Not} {That} {Simple}: {An} {Empirical} {Study} of the {Accuracy}-{Explainability} {Trade}-off in {Machine} {Learning} for {Public} {Policy}},
	isbn = {9781450393522},
	shorttitle = {It’s {Just} {Not} {That} {Simple}},
	url = {https://dl.acm.org/doi/10.1145/3531146.3533090},
	doi = {10.1145/3531146.3533090},
	language = {en},
	urldate = {2025-09-11},
	booktitle = {2022 {ACM} {Conference} on {Fairness} {Accountability} and {Transparency}},
	publisher = {ACM},
	author = {Bell, Andrew and Solano-Kamaiko, Ian and Nov, Oded and Stoyanovich, Julia},
	month = jun,
	year = {2022},
	pages = {248--266},
}

@misc{gilpin_explaining_2019,
	title = {Explaining {Explanations}: {An} {Overview} of {Interpretability} of {Machine} {Learning}},
	shorttitle = {Explaining {Explanations}},
	url = {http://arxiv.org/abs/1806.00069},
	doi = {10.48550/arXiv.1806.00069},
	abstract = {There has recently been a surge of work in explanatory artificial intelligence (XAI). This research area tackles the important problem that complex machines and algorithms often cannot provide insights into their behavior and thought processes. XAI allows users and parts of the internal system to be more transparent, providing explanations of their decisions in some level of detail. These explanations are important to ensure algorithmic fairness, identify potential bias/problems in the training data, and to ensure that the algorithms perform as expected. However, explanations produced by these systems is neither standardized nor systematically assessed. In an effort to create best practices and identify open challenges, we provide our definition of explainability and show how it can be used to classify existing literature. We discuss why current approaches to explanatory methods especially for deep neural networks are insufficient. Finally, based on our survey, we conclude with suggested future research directions for explanatory artificial intelligence.},
	urldate = {2025-09-10},
	publisher = {arXiv},
	author = {Gilpin, Leilani H. and Bau, David and Yuan, Ben Z. and Bajwa, Ayesha and Specter, Michael and Kagal, Lalana},
	month = feb,
	year = {2019},
	note = {arXiv:1806.00069 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning},
}

@misc{morgan_explainable_2023,
	title = {Explainable {AI}: {Visualizing} {Attention} in {Transformers}},
	shorttitle = {Explainable {AI}},
	url = {https://www.comet.com/site/blog/explainable-ai-for-transformers/},
	abstract = {Learn how to visualize the attention of transformers and log your results to Comet, as we work towards explainability in AI.},
	language = {en-US},
	urldate = {2025-08-21},
	journal = {Comet},
	author = {Morgan, Abby},
	month = jul,
	year = {2023},
	keywords = {outil BERT},
}

@inproceedings{garouani_investigating_2024,
	title = {Investigating the {Duality} of {Interpretability} and {Explainability} in {Machine} {Learning}},
	url = {http://arxiv.org/abs/2503.21356},
	doi = {10.1109/ICTAI62512.2024.00125},
	abstract = {The rapid evolution of machine learning (ML) has led to the widespread adoption of complex "black box" models, such as deep neural networks and ensemble methods. These models exhibit exceptional predictive performance, making them invaluable for critical decision-making across diverse domains within society. However, their inherently opaque nature raises concerns about transparency and interpretability, making them untrustworthy decision support systems. To alleviate such a barrier to high-stakes adoption, research community focus has been on developing methods to explain black box models as a means to address the challenges they pose. Efforts are focused on explaining these models instead of developing ones that are inherently interpretable. Designing inherently interpretable models from the outset, however, can pave the path towards responsible and beneficial applications in the field of ML. In this position paper, we clarify the chasm between explaining black boxes and adopting inherently interpretable models. We emphasize the imperative need for model interpretability and, following the purpose of attaining better (i.e., more effective or efficient w.r.t. predictive performance) and trustworthy predictors, provide an experimental evaluation of latest hybrid learning methods that integrates symbolic knowledge into neural network predictors. We demonstrate how interpretable hybrid models could potentially supplant black box ones in different domains.},
	urldate = {2025-08-21},
	booktitle = {2024 {IEEE} 36th {International} {Conference} on {Tools} with {Artificial} {Intelligence} ({ICTAI})},
	author = {Garouani, Moncef and Mothe, Josiane and Barhrhouj, Ayah and Aligon, Julien},
	month = oct,
	year = {2024},
	note = {arXiv:2503.21356 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Machine Learning},
	pages = {861--867},
}

@article{huang_explainable_2024,
	title = {From explainable to interpretable deep learning for natural language processing in healthcare: {How} far from reality?},
	volume = {24},
	issn = {2001-0370},
	shorttitle = {From explainable to interpretable deep learning for natural language processing in healthcare},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11126530/},
	doi = {10.1016/j.csbj.2024.05.004},
	abstract = {Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent model interpretability, or at least explainability, for reliable decision-making. This work presents a thorough scoping review of explainable and interpretable DL in healthcare NLP. The term “eXplainable and Interpretable Artificial Intelligence” (XIAI) is introduced to distinguish XAI from IAI. Different models are further categorized based on their functionality (model-, input-, output-based) and scope (local, global). Our analysis shows that attention mechanisms are the most prevalent emerging IAI technique. The use of IAI is growing, distinguishing it from XAI. The major challenges identified are that most XIAI does not explore “global” modelling processes, the lack of best practices, and the lack of systematic evaluation and benchmarks. One important opportunity is to use attention mechanisms to enhance multi-modal XIAI for personalized medicine. Additionally, combining DL with causal logic holds promise. Our discussion encourages the integration of XIAI in Large Language Models (LLMs) and domain-specific smaller models. In conclusion, XIAI adoption in healthcare requires dedicated in-house expertise. Collaboration with domain experts, end-users, and policymakers can lead to ready-to-use XIAI methods across NLP and medical tasks. While challenges exist, XIAI techniques offer a valuable foundation for interpretable NLP algorithms in healthcare.},
	urldate = {2025-08-21},
	journal = {Computational and Structural Biotechnology Journal},
	author = {Huang, Guangming and Li, Yingya and Jameel, Shoaib and Long, Yunfei and Papanastasiou, Giorgos},
	month = may,
	year = {2024},
	pmid = {38800693},
	pmcid = {PMC11126530},
	pages = {362--373},
}

@misc{mohammadi_explainability_2025,
	title = {Explainability in {Practice}: {A} {Survey} of {Explainable} {NLP} {Across} {Various} {Domains}},
	shorttitle = {Explainability in {Practice}},
	url = {http://arxiv.org/abs/2502.00837},
	doi = {10.48550/arXiv.2502.00837},
	abstract = {Natural Language Processing (NLP) has become a cornerstone in many critical sectors, including healthcare, finance, and customer relationship management. This is especially true with the development and use of advanced models such as GPT-based architectures and BERT, which are widely used in decision-making processes. However, the black-box nature of these advanced NLP models has created an urgent need for transparency and explainability. This review explores explainable NLP (XNLP) with a focus on its practical deployment and real-world applications, examining its implementation and the challenges faced in domain-specific contexts. The paper underscores the importance of explainability in NLP and provides a comprehensive perspective on how XNLP can be designed to meet the unique demands of various sectors, from healthcare's need for clear insights to finance's emphasis on fraud detection and risk assessment. Additionally, this review aims to bridge the knowledge gap in XNLP literature by offering a domain-specific exploration and discussing underrepresented areas such as real-world applicability, metric evaluation, and the role of human interaction in model assessment. The paper concludes by suggesting future research directions that could enhance the understanding and broader application of XNLP.},
	urldate = {2025-08-21},
	publisher = {arXiv},
	author = {Mohammadi, Hadi and Bagheri, Ayoub and Giachanou, Anastasia and Oberski, Daniel L.},
	month = feb,
	year = {2025},
	note = {arXiv:2502.00837 [cs]
version: 1},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language},
}

@article{mersha_evaluating_2025,
	title = {Evaluating the {Effectiveness} of {XAI} {Techniques} for {Encoder}-{Based} {Language} {Models}},
	volume = {310},
	issn = {09507051},
	url = {http://arxiv.org/abs/2501.15374},
	doi = {10.1016/j.knosys.2025.113042},
	abstract = {The black-box nature of large language models (LLMs) necessitates the development of eXplainable AI (XAI) techniques for transparency and trustworthiness. However, evaluating these techniques remains a challenge. This study presents a general evaluation framework using four key metrics: Human-reasoning Agreement (HA), Robustness, Consistency, and Contrastivity. We assess the effectiveness of six explainability techniques from five different XAI categories model simplification (LIME), perturbation-based methods (SHAP), gradient-based approaches (InputXGradient, Grad-CAM), Layer-wise Relevance Propagation (LRP), and attention mechanisms-based explainability methods (Attention Mechanism Visualization, AMV) across five encoder-based language models: TinyBERT, BERTbase, BERTlarge, XLM-R large, and DeBERTa-xlarge, using the IMDB Movie Reviews and Tweet Sentiment Extraction (TSE) datasets. Our findings show that the model simplification-based XAI method (LIME) consistently outperforms across multiple metrics and models, significantly excelling in HA with a score of 0.9685 on DeBERTa-xlarge, robustness, and consistency as the complexity of large language models increases. AMV demonstrates the best Robustness, with scores as low as 0.0020. It also excels in Consistency, achieving near-perfect scores of 0.9999 across all models. Regarding Contrastivity, LRP performs the best, particularly on more complex models, with scores up to 0.9371.},
	urldate = {2025-08-21},
	journal = {Knowledge-Based Systems},
	author = {Mersha, Melkamu Abay and Yigezu, Mesay Gemeda and Kalita, Jugal},
	month = feb,
	year = {2025},
	note = {arXiv:2501.15374 [cs]},
	keywords = {Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning},
	pages = {113042},
}
