Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell

• URL: http://arxiv.org/abs/2406.14673v2
• Date: Fri, 04 Oct 2024 22:14:09 GMT
• Title: Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell
• Authors: Taiming Lu, Muhan Gao, Kuai Yu, Adam Byerly, Daniel Khashabi,
• Abstract summary: Large Language Models (LLMs) exhibit positional bias, struggling to utilize information from the middle or end of long contexts. We find that while LLMs encode the position of target information, they often fail to leverage this in generating accurate responses.
• Score: 14.146413770229392
• License:
• Abstract: Large Language Models (LLMs) exhibit positional bias, struggling to utilize information from the middle or end of long contexts. Our study explores LLMs' long-context reasoning by probing their hidden representations. We find that while LLMs encode the position of target information, they often fail to leverage this in generating accurate responses. This reveals a disconnect between information retrieval and utilization, a "know but don't tell" phenomenon. We further analyze the relationship between extraction time and final accuracy, offering insights into the underlying mechanics of transformer models.

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