Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
AI Research
arXiv CS.AI·16d ago·5 min read
Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
TL;DR
Coalition formation among agents is a complex problem in multi-agent systems. This study introduces a framework using large language model (LLM) agents to simulate and audit coalition dynamics.
✦ Why It Matters
Engineers can implement LLM agents in their systems to enhance collaborative decision-making processes today.
Key Takeaways
- Explore coalition dynamics using the developed multi-agent framework.
- Utilize DPO for instilling party-specific ideologies in LLMs.
- Implement MILT for tracing negotiation outcomes to party manifestos.
- Benchmark simulated agreements against historical coalition data.
How It Works
The framework combines SFT, DPO, and RAG to create LLM agents that reflect specific party ideologies while ensuring factual accuracy. DPO enhances the agents' partisan behavior, while RAG maintains alignment with official party manifestos.
This allows for realistic simulations of coalition negotiations, where agents interact in a structured environment overseen by a formateur.