Evaluating and Analyzing the Coordination Abilities of Large Language Models in Pure Coordination Games

Core Concepts

Large Language Models (LLMs) demonstrate promising capabilities for developing coordination agents, but the requisite conditions, strengths, and limitations of LLMs in coordination games remain unclear.

Abstract

The paper introduces the LLM-Coordination Benchmark to evaluate and analyze the multi-agent coordination abilities of LLMs. The benchmark consists of two tasks:

Agentic Coordination: LLMs participate as agents in end-to-end pure coordination games, including Hanabi, Overcooked-AI, Collab Capture, and Collab Escape. This task provides a holistic evaluation of LLMs' competencies as coordination agents.

Coordination QA: LLMs answer curated multiple-choice questions about edge-case scenarios from the coordination games, focusing on three key areas - Environment Comprehension, Theory of Mind Reasoning, and Joint Planning.

The authors also present the Cognitive Architecture for Coordination (CAC) framework, which enables LLMs to effectively participate in complex, partially observable coordination games like Hanabi.
The experiments show that LLM agents equipped with GPT-4-turbo can match or outperform state-of-the-art reinforcement learning methods in games that require commonsense actions based on the environment. However, LLMs struggle in more complex settings that demand advanced Theory of Mind reasoning and joint planning abilities. The analysis also reveals that LLM agents are more robust to unseen partners compared to self-play trained RL agents.
The Coordination QA results highlight a significant performance gap between GPT-4-turbo and other LLMs across the three question types. LLMs are most proficient in Environment Comprehension, but face substantial challenges in Theory of Mind Reasoning and Joint Planning, underscoring the critical importance of these abilities for effective coordination.

Stats

Overcooked Layouts
CR: 173.3 ± 6.67
AA: 260.0 ± 11.55
Ring: 140.0 ± 0.00
FC: 180.0 ± 11.55
CC: 160.0 ± 0.00
Hanabi Challenge
GPT-4-turbo: 13.33 ± 0.88
GPT-3.5-turbo: 1.33 ± 0.72
Mixtral-8x7b: 0.33 ± 0.27

Quotes

"LLM agents equipped with GPT-4-turbo achieve comparable performance to state-of-the-art reinforcement learning methods in games that require commonsense actions based on the environment."
"LLM agents are capable of collaborating with new partners, unlike self-play MARL methods (Carroll et al., 2019a; Bard et al., 2020) that fail to adapt to unseen agents."
"LLMs are most proficient in Environment Comprehension, indicating they understand game rules and states well. However, they face significant challenges in Theory of Mind Reasoning, with difficulty inferring others' intentions and needs. This issue worsens in Joint Planning, where most LLMs underperform, some even worse than random choices."

Key Insights Distilled From

LLM-Coordination

by Saaket Agash... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2310.03903.pdf

Deeper Inquiries

How can the coordination abilities of LLMs be further improved, especially in the areas of Theory of Mind Reasoning and Joint Planning?

To enhance the coordination abilities of Large Language Models (LLMs) in Theory of Mind Reasoning and Joint Planning, several strategies can be implemented. Firstly, incorporating more diverse and complex training data that includes a wide range of scenarios requiring advanced Theory of Mind reasoning can help LLMs better understand and predict the intentions and beliefs of their partners. Additionally, fine-tuning the models specifically for Theory of Mind tasks and Joint Planning can improve their performance in these areas. Implementing specialized modules within the LLM architecture dedicated to Theory of Mind reasoning and Joint Planning can also enhance their capabilities in these domains. Lastly, continual reinforcement learning and exposure to challenging coordination tasks can further refine the LLMs' abilities in Theory of Mind Reasoning and Joint Planning.

What are the potential drawbacks or limitations of using LLMs as coordination agents compared to other AI approaches, and how can these be addressed?

While LLMs show promise as coordination agents, they also have certain limitations compared to other AI approaches. One drawback is the computational complexity and latency associated with LLMs, making real-time coordination challenging. Additionally, LLMs may struggle with interpreting subtle cues and context in coordination tasks, leading to suboptimal performance in Theory of Mind reasoning. To address these limitations, optimizing the architecture and algorithms of LLMs for faster processing and response times can help mitigate the latency issue. Providing more diverse and nuanced training data can improve the LLMs' understanding of complex coordination scenarios, enhancing their Theory of Mind reasoning abilities. Collaborating LLMs with specialized AI agents that excel in specific coordination tasks can also complement their capabilities and address their limitations.

Given the strong correlation between Environment Comprehension, Theory of Mind Reasoning, and Joint Planning, how can these cognitive abilities be developed in a more integrated and holistic manner within LLMs?

To develop Environment Comprehension, Theory of Mind Reasoning, and Joint Planning in a more integrated and holistic manner within LLMs, a comprehensive training approach is essential. Firstly, designing training tasks that require the simultaneous application of these cognitive abilities can help LLMs learn to integrate them effectively. Providing feedback mechanisms that reinforce the correct application of these abilities can further enhance their integration. Incorporating multi-task learning frameworks that focus on all three cognitive abilities simultaneously can promote a more holistic development within LLMs. Additionally, leveraging reinforcement learning techniques that reward coordinated actions based on accurate Environment Comprehension, Theory of Mind Reasoning, and Joint Planning can strengthen the interconnected development of these cognitive skills in LLMs.