Mechanistic interpretability for enhancing RAG models

Internship - Master 2 ARIAS Minerve

Marine Delvallez

# Introduction & Context

## Retrieval Augmented Generation - RAG

### Retrieval Augmented Generation - Definition

Add a knowledge database to a generative model

- increase knowledge reliability
- release the burden of knowledge on the generator

Tasks:

- Knowledge intensive Generation tasks
- Ex: Question Answering, Fact Checking, Dialogue, Retrieval

### Retrieval

Models includes Information Retrieval as a sub-task : Identify documents and informations useful to help generation

2 principal technologies:

- cross-encoder
- bi-encoder

\(\Rightarrow\) Needs indexing before use

### RAG Architectures

Simple RAG Architecture (Lewis et al. 2020)

Advanced RAG Architecture (Fan et al. 2024)

### Examples

RAG Architecture from (Tran et al. 2024)

RAG architecture used in the following

## Explainability in Artificial Intelligence

Open black box models

#### Aims

- Trustability
- Understandability
- Model Rectification

### Definitions

XAI : Make model’s behavior understandable for human (Bell et al. 2022)
Understand : Predict model’s behavior (Bell et al. 2022)
Explanation : Any way to make the decision process of the model understandable for human

Interpretability
How ?

Explanability
Why ?

### Explanation through creation of a model

## Mechanistic Interpretability

### Definition

> Reverse-engineer neural networks by identifying internal components (neurons, circuits, activation pathways, …) that either contribute to or causally influence specific behaviors. (Somvanshi et al. 2026)

Several approches:

- Manual Circuit Tracing
- Intervention-based Techniques
- Representation Analysis
- Toy Models and Synthetic Tasks

### Activation Patching (Chen et al. 2024)

Let \(Q \times D \subset \mathcal{Q}\times\mathcal{D}\) be a set of pairs of questions and documents
Let \(Q \times \tilde{D}\) the same set of pairs but with perturbed documents

1. Forward pass all \(Q\times D\)
   - record \(o_{i,j}^e\) the output of each component \(n_{i,j}, \forall e \in Q\times D\)
   - record \(p_D\) the performance of the model
2. Forward pass all \(Q\times \tilde{D}\)
   - record \(o_{i,j}^\tilde{e}\) the output of each component \(n_{i,j}, \forall \tilde{e} \in Q\times \tilde{D}\)
   - record \(p_\tilde{D}\) the performance of the model
3. Rewrite \(D, e, \tilde{D} \text{ and } \tilde{e}\) as
   - \(\hat{D}, \hat{e}, \check{D} \text{ and } \check{e}\) if \(p_D > p_\tilde{D}\)
   - \(\check{D}, \check{e}, \hat{D} \text{ and } \hat{e}\) otherwise

### Activation Patching (Chen et al. 2024)

3. Rewrite \(D, e, \tilde{D} \text{ and } \tilde{e}\) as
   - \(\hat{D}, \hat{e}, \check{D} \text{ and } \check{e}\) if \(p_D > p_\tilde{D}\)
   - \(\check{D}, \check{e}, \hat{D} \text{ and } \hat{e}\) otherwise

4. For each component \(n_{i,j}\) forward pass \(Q\times\check{D}\) but replace \(o_{i,j}^{\check{e}}\) by \(o_{i,j}^{\hat{e}}\) for each \(\check{e}\). Record the performance \(\bar{p}\)
5. \(P = \frac{\bar{p} - p_\hat{D} }{p_\check{D} - p_\hat{D}}\) gives the impact of the perturbation on the model performance

### Animation de l’execution de Activation patching (Step 0)

### Animation de l’execution de Activation patching (Step 1)

### Animation de l’execution de Activation patching (Step 2)

### Animation de l’execution de Activation patching (Step 3 and 4)

### MechIR (Parry et al. 2025)

- Python library
- Activation Patching
- Information retrieval models
- Charts sensibility of model components to a perturbation

## Use case of my internship

Use case : Domain specific language

Considering that activation patching charts the sensibility of a model to a perturbation

Hypothesis :

- Models have specific components dedicated to domain specific language
- This domain specific language dedicated part of the model can be charted with activation patching
- That map can be used to enhance model comprehension and robustness of the domain specific language dedicated part of the model

# Method

### Building a perturbation

1. Identify domain specific language \(L\) of the dataset
   - List words from each text
   - Calculate frequency and TF-IDF of each word
   - Find the domain specific language
2. Identify polysemic words \(w\) that has a different meaning in everyday language
3. Find a synonym \(s\) of the everyday meaning of each \(w\)
4. Build perturbation \(w \to s\)

### Examples of perturbation

- solution -> answer
  - microwave a measurements of dielectric absorption in dilute solutions
  - microwave a measurements of dielectric absorption in dilute answer
- microwave -> toaster
  - microwave a measurements of dielectric absorption in dilute solutions
  - toaster a measurements of dielectric absorption in dilute solutions

    REPLACE
    Query: [CLS] measurement of dielectric constant of liquids by the use of microwave techniques [SEP]
    Baseline Document: [CLS] broadband millimetre wave paramagnetic resonance spectrometer the specimen and waveguide which can be cooled by means of a cryostat are placed between close pole pieces giving high uniform magnetic fields design details and some measurements on zero field splittings are given [SEP]
    Perturbed Document: [CLS] broadband millimetre wave paramagnetic resonance spectrometer the specimen and waveguide which can be cooled by means of a cryostat are placed between close pole pieces giving high uniform magnetic fields design details and some measurements on zero field splittings are given [SEP]
    ==================================================
    Query: [CLS] measurement of dielectric constant of liquids by the use of microwave techniques [SEP]
    Baseline Document: [CLS] microwave a measurements of dielectric absorption in dilute solutions [SEP]
    Perturbed Document: [CLS] toaster measurements of dielectric absorption in dilute solutions [SEP]
    ==================================================

### Select useful Perturbation

5. Apply Activation Patching
6. Select relevant perturbations

### Chart Model sensibility to the perturbation

7. Map the model sensibility to the perturbation

### Finetune the Model

8. Modify sensible weights

WORK IN PROGRESS

# Experimentation

### Setup - Dataset

- Extracted from EvalLLM2026 Challenge dataset
- French Defense dataset
- Pairs `(question, chunk of document)`
- 52 pairs, 26 questions

### Setup - Model

E5-small (Wang et al. 2022)

- Constrative bi-encoder
- Trained on unlabeled pairs (query, passage) CCPairs
- Based on MiniLM (Wang et al. 2021)
- 33M params

### Setup - Mechanistic Interpretability Tools

Python libraries:

- MechIR (Parry et al. 2025)
- TransformerLens (Nanda and Bloom 2022)

### First Results - Domain specific language identification

- Defense-specific language is prevalent

Word | Frequency | TF-IDF score
--- | --- | ---
militaire | 26 | 0.04494102684257385
drone | 21 | 0.04081921309724857
article | 13 | 0.0317659447734224
défense | 9 | 0.018444049962599192
emploi | 7 | 0.017992160135997803
armée | 7 | 0.0166576415296721
… | … | …

### First results - Perturbations in general

### First results - Best Perturbations

- needs more complex perturbation (grammar, group of words)
- not enough

. . .

LLMs target polysemy problem of models like Word2Vect

### Work left for the next 2,5 months

- Identify new perturbations
  - new words and synonyms
  - new type of perturbation
- Chart the sensibility of the model to new perturbations
- Identify model modifications that enhance domain specific language comprehension/ use

# Conclusion & Perspectives

### Conclusion

- H1 : Models have specific components dedicated to domain specific language
- H2 : This domain specific language dedicated part of the model can be charted with activation patching
- H3 : That map can be used to enhance model comprehension and robustness of the domain specific language dedicated part of the model

\(\Rightarrow\) Work in Progress

### Other perturbations and linguistic structures

- subgroups of domain specific language
- temporal relationships
- negation
- expression of diachrony

### Other use of the map

- Transfert learning
- Prunning
- Specialization of general models

##

Thanks for you attention

Questions

### References

Bell, Andrew, Ian Solano-Kamaiko, Oded Nov, and Julia Stoyanovich. 2022. “It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-Off in Machine Learning for Public Policy.” 2022 ACM Conference on Fairness Accountability and Transparency (Seoul Republic of Korea), June, 248–66. https://doi.org/10.1145/3531146.3533090.
Chen, Catherine, Jack Merullo, and Carsten Eickhoff. 2024. “Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models.” Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (Washington DC USA), July, 1401–10. https://doi.org/10.1145/3626772.3657841.
Fan, Wenqi, Yujuan Ding, Liangbo Ning, et al. 2024. A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models. arXiv. https://doi.org/10.48550/arXiv.2405.06211.
Lewis, Patrick, Ethan Perez, Aleksandra Piktus, et al. 2020. “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.” arXiv: Computation and Language.
Nanda, Neel, and Joseph Bloom. 2022. TransformerLens. https://github.com/TransformerLensOrg/TransformerLens.
Parry, Andrew, Catherine Chen, Carsten Eickhoff, and Sean MacAvaney. 2025. “MechIR: A Mechanistic Interpretability Framework for Information Retrieval.” Advances in Information Retrieval - 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6-10, 2025, Proceedings, Part V, Lecture Notes in Computer Science, vol. 15576: 89–95. https://doi.org/10.1007/978-3-031-88720-8_16.
Somvanshi, Shriyank, Md Monzurul Islam, Amir Rafe, et al. 2026. “Bridging the Black Box: A Survey on Mechanistic Interpretability in AI.” ACM Computing Surveys 58 (8): 1–35. https://doi.org/10.1145/3787104.
Tran, The Trung, Carlos-Emiliano González-Gallardo, and Antoine Doucet. 2024. “Retrieval Augmented Generation for Historical Newspapers.” Proceedings of the 24th ACM/IEEE Joint Conference on Digital Libraries (Hong Kong China), December, 1–5. https://doi.org/10.1145/3677389.3702542.
Wang, Liang, Nan Yang, Xiaolong Huang, et al. 2022. Text Embeddings by Weakly-Supervised Contrastive Pre-Training. https://arxiv.org/abs/2212.03533v2.
Wang, Wenhui, Hangbo Bao, Shaohan Huang, Li Dong, and Furu Wei. 2021. “MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers.” In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, edited by Chengqing Zong, Fei Xia, Wenjie Li, and Roberto Navigli. Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-acl.188.