Publication
Cooperation, Competition, and Maliciousness: LLM-Stakeholders Interactive Negotiation
Sahar Abdelnabi; Amr Gomaa; Sarath Sivaprasad; Lea Schönherr; Mario Fritz
In: Neural Information Processing Systems (NeurIPS). Neural Information Processing Systems (NeurIPS-2024), located at The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track, December 10-15, Vancouver, British Columbia, Canada, NeurIPS, 2024.
Abstract
There is a growing interest in using Large Language Models (LLMs) in multi-agent systems to tackle interactive real-world tasks that require effective collaboration and assessment of complex situations. Yet, we have a limited understanding of LLMs' communication and decision-making abilities in multi-agent setups. The fundamental task of negotiation spans many key features of communication, such as cooperation, competition, and manipulation potentials. Thus, we propose using scorable negotiation to evaluate LLMs. We create a testbed of complex multi-agent, multi-issue, and semantically rich negotiation games. To reach an agreement, agents must have strong arithmetic, inference, exploration, and planning capabilities while integrating them in a dynamic and multi-turn setup. We propose metrics to rigorously quantify agents' performance and alignment with the assigned role. We provide procedures to create new games and increase the difficulty of games to have an evolving benchmark. Importantly, we evaluate critical safety aspects such as the interaction dynamics between agents influenced by greedy and adversarial players. Our benchmark is highly challenging; GPT-3.5 and small models mostly fail, and GPT-4 and SoTA large models (e.g., Llama-3 70b) still underperform in reaching agreement in non-cooperative and more difficult games. The benchmark is available at "https://github.com/S-Abdelnabi/LLM-Deliberation/".