Papers with mechanistic interpretability tools
From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models (2026.acl-long)
Copied to clipboard
Ling Shi, Xinwei Wu, Xiaohu Zhao, Hao Wang, Heng Liu, Yangyang Liu, Linlong Xu, Longyue Wang, Deyi Xiong, Weihua Luo
| Challenge: | Recent research in mechanistic interpretability has revealed that Large Language models contain disentangled, human-understandable components. |
| Approach: | They propose a framework that first identifies causal task features through frequency recall and interventional filtering, then selects “Feature-Resonant Data” that maximally activates task features for fine-tuning. |
| Outcome: | The proposed framework outperforms existing models on mathematical reasoning, summarization, and translation tasks while using only 50% of the data. |
Preference Tuning For Toxicity Mitigation Generalizes Across Languages (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Detoxifying multilingual Large Language Models (LLMs) has become crucial due to their increasing global use. |
| Approach: | They propose to use English preference tuning to study cross-lingual detoxification of LLMs. |
| Outcome: | The proposed method reduces toxicity in multilingual LLMs by reducing the probability of mGPT-1.3B generating toxic continuations across 17 languages. |
Performance Gap in Entity Knowledge Extraction Across Modalities in Vision Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Vision-language models excel at extracting and reasoning about information from images, yet their capacity to leverage internal knowledge about specific entities remains underexplored. |
| Approach: | They propose a dataset which allows separating entity recognition and question answering . they hypothesize that this decline arises from limitations in how information flows from image tokens to query tokens. |
| Outcome: | The proposed model performance drops when the entity is presented visually rather than textually. |