Reasoning Is Not All You Need: Examining LLMs for Multi-Turn Mental Health Conversations

Jul 1, 2026·
Mohit Chandra
,
Siddharth Sriraman
,
Harneet Singh Khanuja
Yiqiao Jin
Yiqiao Jin
,
Munmun De Choudhury
· 1 min read
Abstract
Mental health support over multi-turn conversations stresses LLM reasoning, empathy, and safety in distinct ways. We systematically examine the limits of reasoning-focused LLMs in multi-turn mental health conversations, isolating failure modes that pure reasoning cannot address, and discuss design implications for responsible deployment. ACL 2026 acceptance rate: 19.0%.
Type
Publication
Annual Meeting of the Association for Computational Linguistics (ACL) 2026, Main Conference

Abstract

Mental health support over multi-turn conversations stresses LLM reasoning, empathy, and safety in distinct ways. We systematically examine the limits of reasoning-focused LLMs in multi-turn mental health conversations, isolating failure modes that pure reasoning cannot address.

Yiqiao Jin
Authors
Ph.D. Candidate in Computer Science
My research focuses on adaptive and efficient AI systems, with emphasis on LLM agents, agent memory, self-distillation, multimodal LLMs, and structured multi-agent intelligence.