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

• URL: http://arxiv.org/abs/2501.09959v1
• Date: Fri, 17 Jan 2025 05:21:49 GMT
• Title: A Survey on Multi-Turn Interaction Capabilities of Large Language Models
• Authors: Chen Zhang, Xinyi Dai, Yaxiong Wu, Qu Yang, Yasheng Wang, Ruiming Tang, Yong Liu,
• Abstract summary: Multi-turn interaction in the dialogue system research refers to a system's ability to maintain context across multiple dialogue turns. Recent advancements in large language models (LLMs) have significantly expanded the scope of multi-turn interaction.
• Score: 47.05742294162551
• License:
• 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.

Related papers

• An LLM Benchmark for Addressee Recognition in Multi-modal Multi-party Dialogue [21.938414385824903]
  This paper focuses on the task of addressee recognition, identifying who is being addressed to take the next turn. A subset of the corpus was annotated with addressee information, revealing that explicit addressees are indicated in approximately 20% of conversational turns.
  arXiv  Detail & Related papers  (2025-01-28T02:27:55Z)
• Unveiling the Impact of Multi-Modal Interactions on User Engagement: A Comprehensive Evaluation in AI-driven Conversations [17.409790984399052]
  This paper explores the impact of multi-modal interactions, which incorporate images and audio alongside text, on user engagement. Our findings reveal a significant enhancement in user engagement with multi-modal interactions compared to text-only dialogues. Results suggest that multi-modal interactions optimize cognitive processing and facilitate richer information comprehension.
  arXiv  Detail & Related papers  (2024-06-21T09:26:55Z)
• Improving Multi-Agent Debate with Sparse Communication Topology [9.041025703879905]
  Multi-agent debate has proven effective in improving large language models quality for reasoning and factuality tasks. In this paper, we investigate the effect of communication connectivity in multi-agent systems. Our experiments on GPT and Mistral models reveal that multi-agent debates leveraging sparse communication topology can achieve comparable or superior performance.
  arXiv  Detail & Related papers  (2024-06-17T17:33:09Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)
• Self-Explanation Prompting Improves Dialogue Understanding in Large Language Models [52.24756457516834]
  We propose a novel "Self-Explanation" prompting strategy to enhance the comprehension abilities of Large Language Models (LLMs) This task-agnostic approach requires the model to analyze each dialogue utterance before task execution, thereby improving performance across various dialogue-centric tasks. Experimental results from six benchmark datasets confirm that our method consistently outperforms other zero-shot prompts and matches or exceeds the efficacy of few-shot prompts.
  arXiv  Detail & Related papers  (2023-09-22T15:41:34Z)
• Interactive Natural Language Processing [67.87925315773924]
  Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within the field of NLP. This paper offers a comprehensive survey of iNLP, starting by proposing a unified definition and framework of the concept.
  arXiv  Detail & Related papers  (2023-05-22T17:18:29Z)
• A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects [100.75759050696355]
  We provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues. We discuss challenges that meet the real-world application needs but require a greater research focus in the future.
  arXiv  Detail & Related papers  (2023-05-04T11:38:49Z)
• Masking Orchestration: Multi-task Pretraining for Multi-role Dialogue Representation Learning [50.5572111079898]
  Multi-role dialogue understanding comprises a wide range of diverse tasks such as question answering, act classification, dialogue summarization etc. While dialogue corpora are abundantly available, labeled data, for specific learning tasks, can be highly scarce and expensive. In this work, we investigate dialogue context representation learning with various types unsupervised pretraining tasks.
  arXiv  Detail & Related papers  (2020-02-27T04:36:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.