arXiv:2605.24432v1 Announce Type: new Abstract: Large Language Model (LLM) interactions are typically underspecified, with users clarifying all necessary details across multiple conversational turns. Yet recent work shows that LLMs perform far worse in this multi-turn setting than in a single turn with same information being available at once, a phenomenon termed "Lost-in-Conversation." However, bridging this gap effectively remains an open problem. Here we introduce Found in Conversation (FiC), a training framework where a model teaches itself to find and recover its single-turn competence gi
The paper addresses a known limitation in Large Language Models (LLMs) regarding multi-turn conversations, indicating a focused effort to improve user interaction and performance in complex tasks.
Improving LLM competence in multi-turn conversations is crucial for developing more reliable and sophisticated AI agents and applications, reducing the 'Lost-in-Conversation' phenomenon.
This framework suggests a path for LLMs to autonomously improve their conversational coherence and instruction following across multiple turns without extensive human intervention.
- · AI developers
- · LLM-powered SaaS companies
- · Users of AI assistants
- · AI research institutions
- · Companies relying on simpler, single-turn LLM interactions
- · Inefficient multi-turn AI frameworks
LLMs will become more effective at understanding and executing complex, multi-step instructions from users.
This improved conversational ability will accelerate the development and deployment of more capable AI agents across various sectors.
Enhanced multi-turn reliability could lead to greater user trust and dependence on AI systems for critical functions, potentially shrinking white-collar workflows further.
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Read at arXiv cs.CL