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arXiv · 2406.14673

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

Taiming Lu·Muhan Gao·Kuai Yu·Adam Byerly·Daniel Khashabi
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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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Taiming Lu, Muhan Gao, Kuai Yu, Adam Byerly, Daniel Khashabi. 2024-06-20. Insights into LLM Long-Context Failures: When Transformers Know but Don't Tell. https://arxiv.org/abs/2406.14673

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