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LLMs struggle to recall info from middle of long contexts

By PulseAugur Editorial · [1 sources] ·

A study by Liu et al. (2023) titled "Lost in the Middle: How Language Models Use Long Contexts" reveals that Large Language Models do not process information in long contexts uniformly. Instead, they exhibit a U-shaped accuracy curve, recalling information reliably from the beginning (primacy) and end (recency) of the context, but struggling significantly with information placed in the middle. This 'middle sag' worsens as the context window increases, meaning a large token window does not equate to full attention utilization. The paper suggests that controlling the position of retrieved chunks, placing the most relevant ones at the edges of the prompt, can mitigate this issue. AI

IMPACT LLMs' difficulty in recalling information from the middle of long contexts necessitates careful prompt engineering and retrieval strategies to ensure full utilization of provided data.

RANK_REASON The cluster discusses findings from a research paper detailing LLM behavior with long contexts. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs struggle to recall info from middle of long contexts

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The cluster discusses findings from a research paper detailing LLM behavior with long contexts. [lever_c_demoted from research: ic=1 ai=1.0]
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  1. dev.to — LLM tag TIER_1 English(EN) · Devanshu Biswas ·

    Lost in the middle: why a 100k-token context window is not 100k tokens of attention

    <p>An LLM does not read a long context evenly. Bury a single fact — a needle — inside a long stack of documents and ask the model to retrieve it, and how reliably it succeeds depends heavily on <em>where</em> the fact sits. Facts at the very start (primacy) or the very end (recen…

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