The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends
On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks
Abstract (arXiv)
Persuasion, as one of the crucial abilities in human communication, has garnered extensive attention from researchers within the field of intelligent dialogue systems. We humans tend to persuade others to change their viewpoints, attitudes or behaviors through conversations in various scenarios (e.g., persuasion for social good, arguing in online platforms). Developing dialogue agents that can persuade others to accept certain standpoints is essential to achieving truly intelligent and anthropomorphic dialogue system. Benefiting from the substantial progress of Large Language Models (LLMs), dialogue agents have acquired an exceptional capability in context understanding and response generation. However, as a typical and complicated cognitive psychological system, persuasive dialogue agents also require knowledge from the domain of cognitive psychology to attain a level of human-like persuasion. Consequently, the cognitive strategy-enhanced persuasive dialogue agent (defined as CogAgent), which incorporates cognitive strategies to achieve persuasive targets through conversation, has become a predominant research paradigm. To depict the research trends of CogAgent, in this paper, we first present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies, including the persuasion strategy, the topic path planning strategy, and the argument structure prediction strategy. Then we propose a new system architecture by incorporating the formalized definition to lay the foundation of CogAgent. Representative works are detailed and investigated according to the combined cognitive strategy, followed by the summary of authoritative benchmarks and evaluation metrics. Finally, we summarize our insights on open issues and future directions of CogAgent for upcoming researchers.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 111 of its 225 references that are in the map. First posted on arXiv 7 Feb 2024.
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- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2023GPT-4 Technical Reportcited by 18,396
- 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
- 2017Graph Attention Networkscited by 8,923
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
- 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
- 2023A Survey of Large Language Modelscited by 2,576
- 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
38 more
- 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
- 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,222
- 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,046
- 2022Code as Policies: Language Model Programs for Embodied Controlcited by 1,210
- 2016ConceptNet 5.5: An Open Multilingual Graph of General Knowledgecited by 1,067
- 2021GLM: General Language Model Pretraining with Autoregressive Blank Infillingcited by 1,031
- 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,013
- 2019DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generationcited by 842
- 2023Can Large Language Models Be an Alternative to Human Evaluations?cited by 734
- 2023Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Modelscited by 668
- 2020Towards Faithfully Interpretable NLP Systems: How should we define and evaluate faithfulness?cited by 599
- 2020Recipes for building an open-domain chatbotcited by 593
- 2021Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Surveycited by 463
- 2019COMET: Commonsense Transformers for Automatic Knowledge Graph Constructioncited by 450
- 2023Is ChatGPT a Good NLG Evaluator? A Preliminary Studycited by 422
- 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 388
- 2023A Survey on Large Language Models for Recommendationcited by 379
- 2023A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPTcited by 368
- 2023GPTScore: Evaluate as You Desirecited by 361
- 2016How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generationcited by 356
- 2021Towards Emotional Support Dialog Systemscited by 299
- 2022Towards a Unified Multi-Dimensional Evaluator for Text Generationcited by 264
- 2023LEVER: Learning to Verify Language-to-Code Generation with Executioncited by 239
- 2022Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMscited by 220
- 2019Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Goodcited by 209
- 2019TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agentscited by 188
- 2022SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualizationcited by 168
- 2018Decoupling Strategy and Generation in Negotiation Dialoguescited by 162
- 2019Challenges in Building Intelligent Open-domain Dialog Systemscited by 147
- 2020A Survey on Conversational Recommender Systemscited by 143
- 2021Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Surveycited by 130
- 2019Survey on Evaluation Methods for Dialogue Systemscited by 126
- 2019Audio-Visual Scene-Aware Dialogcited by 123
- 2020Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusioncited by 122
- 2021Advances and Challenges in Conversational Recommender Systems: A Surveycited by 118
- 2019Towards Knowledge-Based Recommender Dialog Systemcited by 113
- 2016Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-faith Online Discussionscited by 110
- 2020Towards Conversational Recommendation over Multi-Type Dialogscited by 107
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