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    <abstract>Reasoning representations are increasingly used as explanations for large language model outputs. Yet they are typically evaluated with model-centric criteria, such as answer accuracy and faithfulness, leaving it unclear whether they help people evaluate model responses. In this work, we study reasoning representations as human-facing interfaces rather than proxies for model reasoning ability. We conduct a controlled human study of six reasoning formats across tasks of varying complexity, supported by a web-based framework that randomizes task domains, problem instances, and representation order. The study collects fine-grained judgments of structural understanding, error detection and localization, and trust calibration. Our study shows a mismatch between perceived preference and support for human evaluation. Participants prefer planning- and decomposition-based representations, but simpler chain-of-thought traces better support verification, trust, and interpretability. Preferred representations also introduce calibration risks, with more false alarms on correct traces and high trust despite low willingness to verify.</abstract>
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%0 Conference Proceedings
%T Do Reasoning Representations Help Humans Evaluate LLM Outputs?
%A Lim, Jaewoo
%A Shin, Sungbok
%A Hong, Sanghyun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F lim-etal-2026-reasoning
%X Reasoning representations are increasingly used as explanations for large language model outputs. Yet they are typically evaluated with model-centric criteria, such as answer accuracy and faithfulness, leaving it unclear whether they help people evaluate model responses. In this work, we study reasoning representations as human-facing interfaces rather than proxies for model reasoning ability. We conduct a controlled human study of six reasoning formats across tasks of varying complexity, supported by a web-based framework that randomizes task domains, problem instances, and representation order. The study collects fine-grained judgments of structural understanding, error detection and localization, and trust calibration. Our study shows a mismatch between perceived preference and support for human evaluation. Participants prefer planning- and decomposition-based representations, but simpler chain-of-thought traces better support verification, trust, and interpretability. Preferred representations also introduce calibration risks, with more false alarms on correct traces and high trust despite low willingness to verify.
%U https://aclanthology.org/2026.findings-emnlp.521/
%P 10914-10932

Markdown (Informal)

[Do Reasoning Representations Help Humans Evaluate LLM Outputs?](https://aclanthology.org/2026.findings-emnlp.521/) (Lim et al., Findings 2026)

• Do Reasoning Representations Help Humans Evaluate LLM Outputs? (Lim et al., Findings 2026)

ACL

• Jaewoo Lim, Sungbok Shin, and Sanghyun Hong. 2026. Do Reasoning Representations Help Humans Evaluate LLM Outputs?. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 10914–10932, Budapest, Hungary. Association for Computational Linguistics.