NovelHopQA: Diagnosing Multi-Hop Reasoning Failures in Long Narrative Contexts (2025.emnlp-main)
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| Challenge: | Current large language models struggle to answer questions that span tens of thousands of tokens. |
| Approach: | They evaluate 1–4 hop QA over 64k–128k-token excerpts from 83 novels . they find consistent accuracy drops with increased hops and context length . |
| Outcome: | The novelhopqa benchmark evaluates 1–4 hop QA over 64k–128k-token excerpts from 83 public-domain novels. |
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| Challenge: | Existing benchmarks for multi-hop reasoning in biomedical domain are lacking . bioHopR provides benchmarks to evaluate multi-step reasoning in structured biomedic knowledge graphs . |
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| Challenge: | Existing multi-hop question answering datasets do not provide a complete explanation for the reasoning process from the question to the answer. |
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| Challenge: | State-of-the-art Large Language Models (LLMs) are accredited with a number of different capabilities, including reading comprehension, mathematical and reasoning skills, and possessing scientific knowledge. |
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| Challenge: | Recent advances in long-context modeling have enhanced language models for complex tasks, but they struggle with multi-hop reasoning and noisy contexts. |
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Dawei Zhu, Xiyu Wei, Guangxiang Zhao, Wenhao Wu, Haosheng Zou, Junfeng Ran, null XWang, Lin Sun, Xiangzheng Zhang, Sujian Li
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| Challenge: | Existing studies have identified a position bias in Large Language Models that causes them to overlook information at certain positions. |
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| Challenge: | Existing long-context evaluation methods measure surface-level retrieval capabilities, but do not assess performance on the more challenging task of synthesizing distant and underlying information. |
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