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

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Taiming Lu , Muhan Gao , Kuai Yu , Adam Byerly , Daniel Khashabi ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2406.14673

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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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2406.14673
PDF: https://arxiv.org/pdf/2406.14673
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4399986568

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DOI: https://doi.org/10.48550/arxiv.2406.14673

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

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-06-20

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Authors: Taiming Lu, Muhan Gao, Kuai Yu, Adam Byerly, Daniel Khashabi

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Landing page: https://arxiv.org/abs/2406.14673

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PDF URL: https://arxiv.org/pdf/2406.14673

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Open access: Yes

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OA status: green

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OA URL: https://arxiv.org/pdf/2406.14673

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Concepts: Psychology, Computer science

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Cited by: 0

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Related works (count): 10

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