Retrieval Quality at Context Limit
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 16 references that are in the map. First posted on arXiv 8 Nov 2025.
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7 of 16 references.
References · 7
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,655
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
- 2024Gemini 1.5: Unlocking multimodal understanding across millions of tokens of contextcited by 3,175
- 2021Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolationcited by 1,085
- 2023Extending Context Window of Large Language Models via Positional Interpolationcited by 721
- 2024Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encodingcited by 72
- 2024Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Modelscited by 53
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