Papers by Yoav Gur-Arieh
Precise In-Parameter Concept Erasure in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing methods for erasing concepts from model parameters are either too coarse, shallow or ineffective. |
| Approach: | They propose a framework for precisely erasing entire concepts from model parameters by directly editing directions that encode them in parameter space. |
| Outcome: | The proposed framework reduces errors on the target concept to as low as 7.7% while improving specificity and robustness. |
Enhancing Automated Interpretability with Output-Centric Feature Descriptions (2025.acl-long)
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| Challenge: | Existing pipelines for describing concepts in large language models (LLMs) rely on activating inputs to generate descriptions that fail to capture the causal effect of a feature on outputs. |
| Approach: | They propose to use inputs that activate features to generate natural language descriptions for concepts represented by large language models (LLMs) . identifying activating inputs is costly, and the mechanistic role of a feature in model behavior is determined by how inputs cause it to activate and how feature activation affects outputs. |
| Outcome: | The proposed methods capture the causal effect of a feature on outputs better than input-centric descriptions, but combine the two leads to the best performance on both input and output evaluations. |