Workshop on Multi-Turn Interactions in Large Language Models

The field of AI is entering a new era of interaction, profoundly shaped by the capabilities of Large Language Models (LLMs). While multi-turn interaction has been a long-standing pursuit in AI—from dialogue systems to multi-agent coordination—the advent of LLMs has radically transformed this landscape. These models now engage in complex, long-horizon interactions, process diverse data, and make crucial decisions in dynamic, human-centric scenarios.

This leap forward, however, brings forth critical new research questions and challenges that demand immediate attention:

Multi-Turn RL Learning for Agentic Tasks Learning from complex, interactive environments like GUI agents and tool-use scenarios, given the challenges of sparse rewards.
Maintaining Alignment Understanding human values over extended, multi-turn interactions, preventing "loss of alignment" seen in current models.
Human-AI Interaction Over time, ensuring models adapt to user goals without compromising safety or fairness.
Long-horizon Evaluation For LLMs' long-term capabilities, consistency, and strategic abilities in complex, multi-turn tasks.
The Workshop on Multi-Turn Interactions in LLMs is designed to be the central forum for addressing these pivotal questions. We invite researchers to contribute to defining the next generation of interactive AI, tackling these core challenges, and charting the course for future advancements in AI reasoning and planning. This workshop will concentrate on key areas where the extended use of LLMs presents both new challenges and opportunities, serving as a platform to discuss and refine methods for future improvements and evaluation for practical LLM use cases.