Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders

• URL: http://arxiv.org/abs/2507.06427v1
• Date: Tue, 08 Jul 2025 22:17:52 GMT
• Title: Exploring Task Performance with Interpretable Models via Sparse Auto-Encoders
• Authors: Shun Wang, Tyler Loakman, Youbo Lei, Yi Liu, Bohao Yang, Yuting Zhao, Dong Yang, Chenghua Lin,
• Abstract summary: Large Language Models (LLMs) are traditionally viewed as black-box algorithms.<n>In this work, we apply an effective LLM decomposition method using a dictionary-learning approach with sparse autoencoders.
• Score: 17.66934724195822
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large Language Models (LLMs) are traditionally viewed as black-box algorithms, therefore reducing trustworthiness and obscuring potential approaches to increasing performance on downstream tasks. In this work, we apply an effective LLM decomposition method using a dictionary-learning approach with sparse autoencoders. This helps extract monosemantic features from polysemantic LLM neurons. Remarkably, our work identifies model-internal misunderstanding, allowing the automatic reformulation of the prompts with additional annotations to improve the interpretation by LLMs. Moreover, this approach demonstrates a significant performance improvement in downstream tasks, such as mathematical reasoning and metaphor detection.

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