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 BOULDER, a new dynamic benchmark covering eight travel-related tasks that require arithmetic, spatial, and temporal reasoning wit...
| Authors | Ivan Kartáč, Mateusz Lango, Ondřej Dušek |
|---|---|
| Primary category | cs.CL |
| Published | 20 March 2026 |
| Last updated at source | 29 April 2026 |
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