CHORUS: An Agentic Framework for Generating Realistic Deliberation Data
📰 ArXiv cs.AI
Learn how to generate realistic deliberation data using CHORUS, an agentic framework that leverages LLM-powered actors with behaviorally consistent personas
Action Steps
- Implement CHORUS framework using LLM-powered actors
- Configure behaviorally consistent personas for each actor
- Generate realistic deliberation discussions using the framework
- Evaluate the quality of generated data using metrics such as coherence and consistency
- Apply the generated data to train and test models for online discourse analysis
Who Needs to Know This
Researchers and developers working on natural language processing, online discourse analysis, and social media studies can benefit from this framework to generate high-quality deliberation data
Key Insight
💡 CHORUS framework can generate high-quality deliberation data that mimics real-world online discussions
Share This
🤖 Generate realistic deliberation data with CHORUS, an agentic framework that uses LLM-powered actors 📊
Key Takeaways
Learn how to generate realistic deliberation data using CHORUS, an agentic framework that leverages LLM-powered actors with behaviorally consistent personas
Full Article
Title: CHORUS: An Agentic Framework for Generating Realistic Deliberation Data
Abstract:
arXiv:2604.20651v1 Announce Type: new Abstract: Understanding the intricate dynamics of online discourse depends on large-scale deliberation data, a resource that remains scarce across interactive web platforms due to restrictive accessibility policies, ethical concerns and inconsistent data quality. In this paper, we propose Chorus, an agentic framework, which orchestrates LLM-powered actors with behaviorally consistent personas to generate realistic deliberation discussions. Each actor is gove
Abstract:
arXiv:2604.20651v1 Announce Type: new Abstract: Understanding the intricate dynamics of online discourse depends on large-scale deliberation data, a resource that remains scarce across interactive web platforms due to restrictive accessibility policies, ethical concerns and inconsistent data quality. In this paper, we propose Chorus, an agentic framework, which orchestrates LLM-powered actors with behaviorally consistent personas to generate realistic deliberation discussions. Each actor is gove
DeepCamp AI