Panoramic Research on Multi-Turn Dialogue Large Language Models: A Systematic Review from Task Classification to Technical Breakthroughs

This article provides an in-depth interpretation of the review paper 'Beyond Single-Turn: A Survey on Multi-Turn Interactions with Large Language Models' and its supporting open-source resource library Awesome-Multi-Turn-LLMs, systematically organizing multi-turn interaction task classifications, evaluation benchmarks, enhancement methods, and future challenges, and offers a comprehensive technical roadmap for researchers and developers.

After ChatGPT demonstrated its dialogue capabilities, people realized that valuable AI interactions are continuous and coherent multi-turn dialogues. As the performance of LLMs on single-turn tasks has saturated, researchers have turned to the field of multi-turn interactions. This review, completed by researchers from multiple institutions, has been published on arXiv (arXiv:2504.04717), and its supporting GitHub repository contains over 300 related papers, datasets, and code repositories.

Compared to single-turn tasks, multi-turn interactions require maintaining dialogue state, understanding intent evolution, preserving consistency, and avoiding early information forgetting. The core challenges are summarized into four dimensions: context maintenance, coherence preservation, fairness, and response quality, and these challenges amplify exponentially as the number of turns increases.

The review classifies multi-turn LLM tasks into two categories:

Instruction-Following Category

• Multi-turn mathematical reasoning tasks: Gradually clarify problems and revise thinking;
• Code generation and debugging: Iterative collaboration to understand code dependencies;
• Open discussions: Topic advancement and viewpoint development.

Dialogue Participation Category

• Role-playing: Maintain character consistency;
• Medical dialogue: Multi-turn consultation with accuracy and empathy;
• Educational tutoring: Dynamically adjust teaching strategies;
• Security testing and jailbreak prevention: Evaluate model security.

Technical methods are divided into three directions:

Model-Centric Strategies

• In-context learning: Provide multi-turn examples in prompts;
• Supervised Fine-tuning (SFT): Fine-tune with high-quality multi-turn datasets;
• Reinforcement Learning (RL): RLHF/RLAIF to optimize dialogue strategies;
• Architectural innovation: Improve positional encoding, memory modules, etc.

External Information Integration

• Memory enhancement: External memory banks store dialogue history;
• RAG: Retrieve relevant history or external knowledge;
• Knowledge graph integration: Structured storage to support reasoning.

Agent Collaboration

• Single agent: Tool calling, self-reflection;
• Multi-agent: Division of labor and collaboration to improve performance.

Existing evaluation benchmarks are divided into three categories: general-purpose (e.g., MultiWOZ, ConvAI2), domain-specific (mathematics, code, medical), and adversarial (testing security). Current benchmarks have obvious gaps in evaluating the ability of long dialogues (over 20 turns).

Key challenges include:

• Long dialogue memory management: Effectively maintain and retrieve information from hundreds of turns;
• Personalization and adaptability: Learn user habits and preferences;
• Multimodal multi-turn interaction: Incorporate visual and audio information;
• Evaluation methodology: Objectively assess long-term coherence and user satisfaction.

The Awesome-Multi-Turn-LLMs resource library serves as a bridge between academia and industry, providing a literature map for researchers and technical solutions for developers. Multi-turn interaction capability has become a key indicator of a model's practical value, and this field is moving from the exploration phase to a systematic maturity stage.