← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.560 · 记录 #1
Reasoning Gets Harder for LLMs Inside A Dialogue
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:02
摘要
Large Language Models (LLMs) achieve strong performance on many reasoning benchmarks, yet these evaluations typically focus on isolated tasks that differ from real-world usage in task-oriented dialogue (TOD). In this setting, LLMs must perform reasoning inherently while generating text and adhering to instructions on role, format, and style. This mismatch raises concerns about whether benchmark performance accurately reflects models’ reasoning robustness in TOD setting.We investigate how framing reasoning tasks within TOD affects LLM performance by introducing a new dynamic benchmark covering eight travel-related tasks that require arithmetic, spatial, temporal, and commonsense reasoning. Each problem is presented in both isolated and dialogue-based variants, enabling controlled comparison while mitigating data contamination. Experiments on nine LLMs reveal a substantial and consistent performance gap between isolated and dialogue settings. Through ablations and qualitative analysis, we show that this gap is largely driven by the multi-turn nature of dialogue, with additional effects from role conditioning and tool-use requirements. Our results highlight the need to evaluate LLM reasoning in realistic interactive scenarios.
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记录 #1 · 2026-07 · 最新观测版本
Reasoning Gets Harder for LLMs Inside A Dialogue
Large Language Models (LLMs) achieve strong performance on many reasoning benchmarks, yet these evaluations typically focus on isolated tasks that differ from real-world usage in task-oriented dialogue (TOD). In this setting, LLMs must perform reasoning inherently while generating text and adhering to instructions on role, format, and style. This mismatch raises concerns about whether benchmark performance accurately reflects models’ reasoning robustness in TOD setting.We investigate how framing reasoning tasks within TOD affects LLM performance by introducing a new dynamic benchmark covering eight travel-related tasks that require arithmetic, spatial, temporal, and commonsense reasoning. Each problem is presented in both isolated and dialogue-based variants, enabling controlled comparison while mitigating data contamination. Experiments on nine LLMs reveal a substantial and consistent performance gap between isolated and dialogue settings. Through ablations and qualitative analysis, we show that this gap is largely driven by the multi-turn nature of dialogue, with additional effects from role conditioning and tool-use requirements. Our results highlight the need to evaluate LLM reasoning in realistic interactive scenarios.
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