Explainability for Large Language Models: A Survey
Haiyan Zhao, Hanjie Chen, Fan Yang et al.
2024 · ACM Transactions on Intelligent Systems and Technology · 672 citations
Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, understanding and explaining these models is crucial for elucidating their behaviors, limitations, and social impacts. In this article, we introduce a taxonomy of explainability techniques and provide a structured overview of methods for explaining Transformer-based language models. We categorize techniques based on the training paradigms of LLMs: tr…
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