LLM-Deliberation: Evaluating LLMs with Interactive Multi-Agent
  Negotiation Games
Sahar Abdelnabi Amr Gomaa Sarath Sivaprasad Lea Schönherr Mario Fritz
There is a growing interest in using Large Language Models (LLMs) as agents
to tackle real-world tasks that may require assessing complex situations. Yet,
we have a limited understanding of LLMs' reasoning and decision-making
capabilities, partly stemming from a lack of dedicated evaluation benchmarks.
As negotiating and compromising are key aspects of our everyday communication
and collaboration, we propose using scorable negotiation games as a new
evaluation framework for LLMs. We create a testbed of diverse text-based,
multi-agent, multi-issue, semantically rich negotiation games, with easily
tunable difficulty. To solve the challenge, agents need to have strong
arithmetic, inference, exploration, and planning capabilities, while seamlessly
integrating them. Via a systematic zero-shot Chain-of-Thought prompting (CoT),
we show that agents can negotiate and consistently reach successful deals. We
quantify the performance with multiple metrics and observe a large gap between
GPT-4 and earlier models. Importantly, we test the generalization to new games
and setups. Finally, we show that these games can help evaluate other critical
aspects, such as the interaction dynamics between agents in the presence of
greedy and adversarial players.