LLM-enabled Social Agents
📰 ArXiv cs.AI
Learn how LLM-enabled social agents can participate in social environments through natural language, and how to build them with a stronger foundation in roles, norms, and intentions
Action Steps
- Build a social agent using an LLM to enable natural language communication
- Configure the agent with role-based and norm-aware behaviour to improve social intelligibility
- Test the agent in a simulated social environment to evaluate its participation and deliberation capabilities
- Apply contextual constraints to the agent's decision-making process to enhance its social awareness
- Compare the performance of LLM-enabled social agents with traditional agents in various social scenarios
Who Needs to Know This
AI researchers and engineers working on social agents, human-computer interaction, and natural language processing can benefit from this knowledge to create more socially intelligent agents
Key Insight
💡 LLM-enabled social agents require a stronger foundation in roles, norms, intentions, and contextual constraints to achieve socially intelligible behaviour
Share This
🤖 LLM-enabled social agents can participate in social environments through natural language! 📚 Learn how to build them with stronger foundations in roles, norms, and intentions #LLMs #SocialAgents #AI
Key Takeaways
Learn how LLM-enabled social agents can participate in social environments through natural language, and how to build them with a stronger foundation in roles, norms, and intentions
Full Article
Title: LLM-enabled Social Agents
Abstract:
arXiv:2605.02335v1 Announce Type: cross Abstract: Large Language Models (LLMs) have transformed agent-agent and human-agent interaction by enabling software, physical, and simulation agents to communicate and deliberate through natural language. Yet fluent language use does not by itself yield socially intelligible behaviour. Most current systems remain weakly grounded in roles, norms, intentions, and contextual constraints, limiting their capacity for meaningful participation in social environm
Abstract:
arXiv:2605.02335v1 Announce Type: cross Abstract: Large Language Models (LLMs) have transformed agent-agent and human-agent interaction by enabling software, physical, and simulation agents to communicate and deliberate through natural language. Yet fluent language use does not by itself yield socially intelligible behaviour. Most current systems remain weakly grounded in roles, norms, intentions, and contextual constraints, limiting their capacity for meaningful participation in social environm
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