A Survey on Multi-Turn Interaction Capabilities of Large Language Models

2025-01-17

AI Summary

This study addresses the weak interactive capability of large language models (LLMs) in multi-turn dialogue. Methodologically, it introduces a unified “capability–evaluation–enhancement–evolution” analytical framework—the first to jointly characterize context retention, dynamic response generation, and interactive modeling. The approach integrates dialogue state tracking, long-context modeling, trajectory-driven evaluation metrics, retrieval-augmented generation, and memory mechanisms, yielding a scalable multi-turn evaluation protocol and a collaborative interactive evolution pathway. The work provides the first systematic survey of LLM interactivity, precisely identifying core bottlenecks—including state drift, long-range forgetting, and evaluation misalignment—and offers both theoretical foundations and practical guidelines for applications such as conversational search, intelligent consulting, and interactive pedagogy.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Multi-turn interaction in the dialogue system research refers to a system's ability to maintain context across multiple dialogue turns, enabling it to generate coherent and contextually relevant responses. Recent advancements in large language models (LLMs) have significantly expanded the scope of multi-turn interaction, moving beyond chatbots to enable more dynamic agentic interactions with users or environments. In this paper, we provide a focused review of the multi-turn capabilities of LLMs, which are critical for a wide range of downstream applications, including conversational search and recommendation, consultation services, and interactive tutoring. This survey explores four key aspects: (1) the core model capabilities that contribute to effective multi-turn interaction, (2) how multi-turn interaction is evaluated in current practice, (3) the general algorithms used to enhance multi-turn interaction, and (4) potential future directions for research in this field.

Problem

• Large Language Models
• Multi-turn Dialogue
• Interaction Enhancement

Innovation

• Multi-turn Dialogue
• Large Language Models
• Enhanced Interaction Quality