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A Survey on Sparse Autoencoders: Interpreting the Internal Mechanisms of Large Language Models

Created by
  • Haebom

Author

Dong Shu, Xuansheng Wu, Haiyan Zhao, Daking Rai, Ziyu Yao, Ninghao Liu, Mengnan Du

Outline

This paper presents a comprehensive survey of sparse autoencoders (SAEs), which are emerging as a promising method for understanding the internal mechanisms of large-scale language models (LLMs). We comprehensively cover SAE's technical framework, feature description methods, performance evaluation methods, and practical applications, focusing on SAE's ability to decompose complex features of LLMs into interpretable components.

Takeaways, Limitations

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Takeaways:
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We present the usefulness of SAE and effective utilization strategies for understanding the inner workings of LLM.
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We systematically organize and compare different approaches (input-based and output-based) to describe SAE features.
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Structural and functional indicators are presented to evaluate SAE performance.
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Demonstrates practical application of SAE in understanding and manipulating the operation of LLM.
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Limitations:
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The discussion on the generalizability and limitations of the SAE-based LLM interpretation method presented in the paper may be lacking.
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There may be biases towards specific SAE architectures or training strategies.
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Comparative analysis of SAE application results for different LLM architectures may be lacking.
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Further discussion may be needed regarding the reliability of LLM analysis results using SAE and the subjectivity of the interpretation.
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