NeedleChain: Measuring Intact Long-Context Reasoning Capability of Large Language Models

• URL: http://arxiv.org/abs/2507.22411v1
• Date: Wed, 30 Jul 2025 06:29:50 GMT
• Title: NeedleChain: Measuring Intact Long-Context Reasoning Capability of Large Language Models
• Authors: Hyeonseok Moon, Heuiseok Lim,
• Abstract summary: The Needle-in-a-Haystack benchmark is widely used to evaluate Large Language Models' (LLMs) ability to understand long contexts (LC)<n>We demonstrate that even state-of-the-art models such as GPT-4o struggle to intactly incorporate given contexts made up of solely query-relevant ten sentences.<n>We introduce a novel benchmark, textbfNeedleChain, where the context consists entirely of query-relevant information.
• Score: 7.134358758293254
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Needle-in-a-Haystack (NIAH) benchmark is widely used to evaluate Large Language Models' (LLMs) ability to understand long contexts (LC). It evaluates the capability to identify query-relevant context within extensive query-irrelevant passages. Although this method serves as a widely accepted standard for evaluating long-context understanding, our findings suggest it may overestimate the true LC capability of LLMs. We demonstrate that even state-of-the-art models such as GPT-4o struggle to intactly incorporate given contexts made up of solely query-relevant ten sentences. In response, we introduce a novel benchmark, \textbf{NeedleChain}, where the context consists entirely of query-relevant information, requiring the LLM to fully grasp the input to answer correctly. Our benchmark allows for flexible context length and reasoning order, offering a more comprehensive analysis of LLM performance. Additionally, we propose an extremely simple yet compelling strategy to improve LC understanding capability of LLM: ROPE Contraction. Our experiments with various advanced LLMs reveal a notable disparity between their ability to process large contexts and their capacity to fully understand them. Source code and datasets are available at https://github.com/hyeonseokk/NeedleChain

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