LLM reasoning performance drops significantly in dialogue settings, study finds

A new study published on arXiv introduces BOULDER, a benchmark designed to evaluate large language models' reasoning capabilities within task-oriented dialogue settings. The research found a significant performance drop when models performed reasoning tasks in a conversational context compared to isolated tasks. This decline is attributed to the multi-turn nature of dialogue, role conditioning, and tool-use requirements, highlighting the need for more realistic interactive evaluations. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Highlights the need for evaluating LLM reasoning in realistic interactive scenarios, not just isolated benchmarks.

RANK_REASON Academic paper introducing a new benchmark for evaluating LLM reasoning in dialogue.

Read on arXiv cs.CL →

• arXiv
• BOULDER
• LLMs
• Ivan Kartáč

• paper
• safety