🗣️👥 Awesome Social Agents

For the best experience, we recommend reading this document on the website.

The rise of Large Language Models (LLMs)/foundational models presents new opportunities for simulating complex human social behaviors. As a result, there is a rapidly growing body of work emerging in this domain. We hope to categorize and synergize recent efforts to provide a comprehensive guidebook of social agents weaving together multiple domains, including language, embodiment, and robotics.

Our goal is to offer insights crucial for understanding and harnessing social agents' potential impact on society. We strive to keep these updated regularly and continuously. We greatly appreciate any contributions via PRs, issues, emails, or other methods.

🗂️ Check out the examples of social agents. 📚 Check out the table format of the collected papers here.

📝 We are currently working on a survey paper related to content of this repository. Stay tuned for updates!

This repo supports Python 3.9 and above. In one line, to use a virtual environment, e.g. with anaconda3:

conda create -n awsome-social-agents python=3.9; conda activate awsome-social-agents; python -m install requirements.txt

• Papers
  • Surveys and Overview
  • Environments
    • Text and Speech Environments
    • Embodied Environments
    • Virtual Environments
    • Robotics
  • Modeling
    • In-context Learning
    • Finetuning
    • Reinforcement learning
  • Evaluating social agents
    • Evaluating text social agents
    • Evaluating embodied social agents
    • Evaluating virtual social agents
    • Evaluating robotics in social contexts
  • Interactions with humans
    • Human-Chatbot Interaction
    • Human-Embodied Agent Interaction
    • Human Robot Interaction
    • Human-Human Interaction
  • Challenges
    • Theory of Mind
    • Social Learning
    • Simultaneous Interaction
  • Applications
    • Health
    • Policy
    • Education
  • Concerns
    • Risks
    • Safety

[June, 2023] Socially intelligent machines that learn from humans and help humans learn, Gweon et al., arXiv

[October, 2023] SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents, Xuhui Zhou et al., ICLR

[October, 2023] CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents, Qinlin Zhao et al., arXiv

[December, 2023] RoboTube: Learning Household Manipulation from Human Videos with Simulated Twin Environments, Haoyu Xiong et al., Proceedings of The 6th Conference on Robot Learning

[August, 2022] Do As I Can and Not As I Say: Grounding Language in Robotic Affordances, Michael Ahn et al., arXiv preprint arXiv:2204.01691

[June, 2022] Inner Monologue: Embodied Reasoning through Planning with Language Models, Wenlong Huang et al., arXiv preprint arXiv:2207.05608

[June, 2023] One Policy to Dress Them All: Learning to Dress People with Diverse Poses and Garments, Yufei Wang et al., Robotics: Science and Systems (RSS)

[August, 2023] Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration, Chen Wang et al., arXiv

[March, 2024] Yell At Your Robot: Improving On-the-Fly from Language Corrections, Lucy Xiaoyang Shi et al., arXiv

[April, 2016] Human--robot interaction: status and challenges, Thomas B Sheridan et al., Human factors

[June, 2021] A taxonomy to structure and analyze human--robot interaction, Linda Onnasch et al., International Journal of Social Robotics

[July, 2023] Robotic vision for human-robot interaction and collaboration: A survey and systematic review, Nicole Robinson et al., ACM Transactions on Human-Robot Interaction

[October, 2022] A survey of multi-agent Human--Robot Interaction systems, Abhinav Dahiya et al., Robotics and Autonomous Systems

[March, 2023] Nonverbal Cues in Human Robot Interaction: A Communication Studies Perspective, Jacqueline Urakami et al., J. Hum.-Robot Interact.

[April, 2023] 15 Years of (Who)man Robot Interaction: Reviewing the H in Human-Robot Interaction, Katie Winkle et al., J. Hum.-Robot Interact.

[May, 2023] Voyager: An Open-Ended Embodied Agent with Large Language Models, Guanzhi Wang et al., arXiv

[March, 2023] Language Models can Solve Computer Tasks, Geunwoo Kim et al., arXiv

[September, 2024] LASER: LLM Agent with State-Space Exploration for Web Navigation, Kaixin Ma et al., arXiv

[May, 2023] Hierarchical Prompting Assists Large Language Model on Web Navigation, Abishek Sridhar et al., arXiv

[January, 2024] Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer Control, Longtao Zheng et al., The Twelfth International Conference on Learning Representations

[November, 2023] AdaPlanner: Adaptive Planning from Feedback with Language Models, Haotian Sun et al., Thirty-seventh Conference on Neural Information Processing Systems

[May, 2023] SPRING: Studying the Paper and Reasoning to Play Games, Yue Wu et al., arXiv

[March, 2023] DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents, Varun Nair et al., arXiv

[October, 2023] Understanding HTML with Large Language Models, Izzeddin Gur et al., arXiv
[ May, 2023] Instruction-Finetuned Foundation Models for Multimodal Web Navigation, Hiroki Furuta et al., ICLR 2023 Workshop on Mathematical and Empirical Understanding of Foundation Models

[October, 2023] ReAct: Synergizing Reasoning and Acting in Language Models, Shunyu Yao et al., arXiv

[January, 2024] A Real-World WebAgent with Planning, Long Context Understanding, and Program Synthesis, Izzeddin Gur et al., The Twelfth International Conference on Learning Representations

[November, 2023] From Pixels to {UI} Actions: Learning to Follow Instructions via Graphical User Interfaces, Peter Shaw et al., Thirty-seventh Conference on Neural Information Processing Systems

[January, 2024] GPT-4V(ision) is a Generalist Web Agent, if Grounded, Boyuan Zheng et al., arXiv

[February, 2024] Dual-View Visual Contextualization for Web Navigation, Jihyung Kil et al., arXiv

[October, 2024] SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents, Xuhui Zhou et al., ICLR

[October, 2023] CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents, Qinlin Zhao et al., arXiv

[March, 2024] How Far Are We on the Decision-Making of LLMs? Evaluating LLMs' Gaming Ability in Multi-Agent Environments, Jen-tse Huang et al., arXiv

[August, 2023] ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate, Chi-Min Chan et al., arXiv

[February, 2024] Automatic Evaluation for Mental Health Counseling using LLMs, Anqi Li et al., arXiv

[February, 2024] How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis, Federico Bianchi et al., arXiv

[May, 2023] PersonaLLM: Investigating the Ability of Large Language Models to Express Personality Traits, Hang Jiang et al., NAACL Findings

[February, 2024] Can Large Language Model Agents Simulate Human Trust Behaviors?, Chengxing Xie et al., ArXiv

[January, 2024] LLM Harmony: Multi-Agent Communication for Problem Solving, Sumedh Rasal et al., ArXiv

[November, 2021] A Comprehensive Assessment of Dialog Evaluation Metrics, Yeh et al., The First Workshop on Evaluations and Assessments of Neural Conversation Systems

[July, 2020] {C}onvo{K}it: A Toolkit for the Analysis of Conversations, Chang et al., Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

[March, 2024] Embodied LLM Agents Learn to Cooperate in Organized Teams, Xudong Guo et al., arXiv

[March, 2024] Embodied LLM Agents Learn to Cooperate in Organized Teams, Xudong Guo et al., arXiv | [March, 2024] HumanoidBench: Simulated Humanoid Benchmark for Whole-Body Locomotion and Manipulation, Carmelo Sferrazza et al., arXiv [January, 2003] Theory and evaluation of human robot interactions, J. Scholtz et al., 36th Annual Hawaii International Conference on System Sciences, 2003. Proceedings of the [March, 2006] Common metrics for human-robot interaction, Aaron Steinfeld et al., Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction [July, 2020] Safety bounds in human robot interaction: A survey, Angeliki Zacharaki et al., Safety science [October, 2011] A meta-analysis of factors affecting trust in human-robot interaction, Peter A Hancock et al., Human factors [November, 2009] Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots, Christoph Bartneck et al., [December, 2015] RoboCup@ Home: Analysis and results of evolving competitions for domestic and service robots, Luca Iocchi et al., Artificial Intelligence [December, 2020] Optimization of criterion for objective evaluation of HRI performance that approximates subjective evaluation: a case study in robot competition, Y. Mizuchi et al., Advanced Robotics

[August, 2022] Social Simulacra: Creating Populated Prototypes for Social Computing Systems, Joon Sung Park et al., Proceedings of the 35th Annual ACM Symposium on User Interface Software and Technology

[November, 2024] Do LLMs exhibit human-like response biases? A case study in survey design, Lindia Tjuatja et al., arXiv

[February, 2024] Large language models cannot replace human participants because they cannot portray identity groups, Angelina Wang et al., arXiv

[February, 2024] Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation, Xinyi Mou et al., arXiv

[March, 2024] From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News, Yuhan Liu et al., arXiv