Language Agents as Optimizable Graphs

TL;DR: Techniques unify LLM-based agents as computational graphs, improving problem solvers. Code available at GitHub.

Summary:

• The GPTSwarm framework represents language agents as computational graphs, introducing nodes and edges to model information flow and collaboration between agents.
• Experiments demonstrate the effectiveness of edge optimization in filtering adversarial agents from a swarm and elevating performance to match the baseline level.
• GPTSwarm is developed using Python and PyTorch, providing implementations for Dataset and PromptSet interfaces to facilitate dataset integration for optimization and evaluation.
• The framework encapsulates the abstraction of an external LLM through an interface, allowing for customization and asynchronous computations for task parallelism.

Major Findings:

1. GPTSwarm framework effectively filters adversarial agents and elevates swarm performance.
2. The framework is highly customizable, allowing for dataset integration and asynchronous computations.
3. GPTSwarm demonstrates the potential for various applications involving language agents and optimization processes.

Analysis and Critique:

• The framework’s ability to automatically improve agent prompts and inter-agent orchestration has broad implications for the development and integration of language model agents in various applications.
• The experiments demonstrate the potential of edge optimization in improving the performance of language agent systems and safeguarding swarms against harmful adversaries.
• GPTSwarm’s customizable nature and ability to integrate datasets and manage optimization processes make it a valuable tool for researchers and practitioners in the field of natural language processing and artificial intelligence.

Appendix

Model	gpt-3.5-turbo-1106
Date Generated	2024-02-28
Abstract	https://arxiv.org/abs/2402.16823v2
HTML	https://browse.arxiv.org/html/2402.16823v2
Truncated	True
Word Count	18591