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        <title>Diagnostic Analysis for Large Language Model Reasoning</title>
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            <title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Hung-Yi</namePart>
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    <abstract>Reasoning structures (e.g., chains and graphs) are increasingly used for probabilistic inference, factuality verification, and uncertainty estimation, but their structural reliability is rarely evaluated directly. We test whether this reliability holds through a proposed two-stage diagnostic evaluation in open-ended multi-hop QA. Stage 1 tests different capabilities required to construct reasoning graphs, showing that graph reliability varies with topology, prompt design, and model family. Stage 2 uses uncertainty estimation as a downstream test case. Generated-edge experiments show that graph-based computation is sensitive to upstream graph errors, with performance dropping relative to the gold-graph setting. Gold-graph experiments further show that graph structure provides little benefit when used only as prompt context, but improves the separation of correct from incorrect answers by confidence score and enables node-level diagnostics.</abstract>
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%0 Conference Proceedings
%T Diagnostic Analysis for Large Language Model Reasoning
%A Liu, Xinyu
%A Fu, Dongqi
%A Wu, Jun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-diagnostic
%X Reasoning structures (e.g., chains and graphs) are increasingly used for probabilistic inference, factuality verification, and uncertainty estimation, but their structural reliability is rarely evaluated directly. We test whether this reliability holds through a proposed two-stage diagnostic evaluation in open-ended multi-hop QA. Stage 1 tests different capabilities required to construct reasoning graphs, showing that graph reliability varies with topology, prompt design, and model family. Stage 2 uses uncertainty estimation as a downstream test case. Generated-edge experiments show that graph-based computation is sensitive to upstream graph errors, with performance dropping relative to the gold-graph setting. Gold-graph experiments further show that graph structure provides little benefit when used only as prompt context, but improves the separation of correct from incorrect answers by confidence score and enables node-level diagnostics.
%U https://aclanthology.org/2026.emnlp-main.769/
%P 17842-17867

Markdown (Informal)

[Diagnostic Analysis for Large Language Model Reasoning](https://aclanthology.org/2026.emnlp-main.769/) (Liu et al., EMNLP 2026)

• Diagnostic Analysis for Large Language Model Reasoning (Liu et al., EMNLP 2026)

ACL

• Xinyu Liu, Dongqi Fu, and Jun Wu. 2026. Diagnostic Analysis for Large Language Model Reasoning. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 17842–17867, Budapest, Hungary. Association for Computational Linguistics.