slides au 2/06 soir

.gitignore

@@ -6,8 +6,14 @@
 *.snm
 *.synctex.gz
 *.toc
-*_files/
+#*_files/
 
 # Quarto
 # .html
-# *_files
+# *_files
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+**/.jupyter_cache/
+*.quarto_ipynb_*
+
+# Document à cacher
+seminaireCA/indications_oral.md
+seminaireCA/test/
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seminaireCA/biblio.bib

@@ -129,4 +129,59 @@
 	year = {2023},
 	note = {arXiv:2302.13971},
 	keywords = {Computer Science - Computation and Language},
+}
+
+@misc{nanda_transformerlens_2022,
+	title = {{TransformerLens}},
+	url = {https://github.com/TransformerLensOrg/TransformerLens},
+	author = {Nanda, Neel and Bloom, Joseph},
+	year = {2022},
+}
+
+
+@article{somvanshi_bridging_2026,
+	title = {Bridging the {Black} {Box}: {A} {Survey} on {Mechanistic} {Interpretability} in {AI}},
+	volume = {58},
+	issn = {0360-0300, 1557-7341},
+	shorttitle = {Bridging the {Black} {Box}},
+	url = {https://dl.acm.org/doi/10.1145/3787104},
+	doi = {10.1145/3787104},
+	abstract = {Mechanistic interpretability seeks to reverse-engineer the internal logic of neural networks by uncovering human-understandable circuits, algorithms, and causal structures that drive model behavior. Unlike post hoc explanations that describe what models do, this paradigm focuses on why and how they compute, tracing information flow through neurons, attention heads, and activation pathways. This survey provides a high-level synthesis of the field-highlighting its motivation, conceptual foundations, and methodological taxonomy rather than enumerating individual techniques. We organize mechanistic interpretability across three abstraction layers— 
+              neurons 
+              , 
+              circuits 
+              , and 
+              algorithms 
+              —and three evaluation perspectives: 
+              behavioral 
+              , 
+              counterfactual 
+              , and 
+              causal 
+              . We further discuss representative approaches and toolchains that enable structural analysis of modern AI systems, outlining how mechanistic interpretability bridges theoretical insights with practical transparency. Despite rapid progress, challenges persist in scaling these analyses to frontier models, resolving polysemantic representations, and establishing standardized causal benchmarks. By connecting historical evolution, current methodologies, and emerging research directions, this survey aims to provide an integrative framework for understanding how mechanistic interpretability can support transparency, reliability, and governance in large-scale AI.},
+	language = {en},
+	number = {8},
+	urldate = {2026-06-01},
+	journal = {ACM Computing Surveys},
+	author = {Somvanshi, Shriyank and Islam, Md Monzurul and Rafe, Amir and Tusti, Anannya Ghosh and Chakraborty, Arka and Baitullah, Anika and Chowdhury, Tausif Islam and Alnawmasi, Nawaf and Dutta, Anandi and Das, Subasish},
+	month = jun,
+	year = {2026},
+	pages = {1--35},
+}
+
+
+@inproceedings{wang_minilmv2_2021,
+	address = {Online},
+	title = {{MiniLMv2}: {Multi}-{Head} {Self}-{Attention} {Relation} {Distillation} for {Compressing} {Pretrained} {Transformers}},
+	shorttitle = {{MiniLMv2}},
+	url = {https://aclanthology.org/2021.findings-acl.188/},
+	doi = {10.18653/v1/2021.findings-acl.188},
+	urldate = {2026-06-02},
+	booktitle = {Findings of the {Association} for {Computational} {Linguistics}: {ACL}-{IJCNLP} 2021},
+	publisher = {Association for Computational Linguistics},
+	author = {Wang, Wenhui and Bao, Hangbo and Huang, Shaohan and Dong, Li and Wei, Furu},
+	editor = {Zong, Chengqing and Xia, Fei and Li, Wenjie and Navigli, Roberto},
+	month = aug,
+	year = {2021},
+	pages = {2140--2151},
 }
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