Retrieval Quality at Context Limit

Fuente: arXiv
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Auteur principal: McKinnon, Max
Format: Preprint
Publié: 2025
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author McKinnon, Max
author_facet McKinnon, Max
contents The ability of large language models (LLMs) to recall and retrieve information from long contexts is critical for many real-world applications. Prior work (Liu et al., 2023) reported that LLMs suffer significant drops in retrieval accuracy for facts placed in the middle of large contexts, an effect known as "Lost in the Middle" (LITM). We find the model Gemini 2.5 Flash can answer needle-in-a-haystack questions with great accuracy regardless of document position including when the document is nearly at the input context limit. Our results suggest that the "Lost in the Middle" effect is not present for simple factoid Q\&A in Gemini 2.5 Flash, indicating substantial improvements in long-context retrieval.
format Preprint
id arxiv_https___arxiv_org_abs_2511_05850
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Retrieval Quality at Context Limit
McKinnon, Max
Information Retrieval
Artificial Intelligence
68P20 (Primary), 68T07 (Secondary)
The ability of large language models (LLMs) to recall and retrieve information from long contexts is critical for many real-world applications. Prior work (Liu et al., 2023) reported that LLMs suffer significant drops in retrieval accuracy for facts placed in the middle of large contexts, an effect known as "Lost in the Middle" (LITM). We find the model Gemini 2.5 Flash can answer needle-in-a-haystack questions with great accuracy regardless of document position including when the document is nearly at the input context limit. Our results suggest that the "Lost in the Middle" effect is not present for simple factoid Q\&A in Gemini 2.5 Flash, indicating substantial improvements in long-context retrieval.
title Retrieval Quality at Context Limit
topic Information Retrieval
Artificial Intelligence
68P20 (Primary), 68T07 (Secondary)
url https://arxiv.org/abs/2511.05850