Decision-Making Large Language Model (LLM) Agent

A Decision-Making Large Language Model (LLM) Agent is a LLM-based agent that can solve automated decision-making tasks.

• See: LLM Emergent Property.

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References

2023

• (Shinn et al., 2023) ⇒ Noah Shinn, Beck Labash, and Ashwin Gopinath. (2023). “Reflexion: An Autonomous Agent with Dynamic Memory and Self-reflection.” doi:10.48550/arXiv.2303.11366
  • QUOTE: Recent advancements in decision-making large language model (LLM) agents have demonstrated impressive performance across various benchmarks. However, these state-of-the-art approaches typically necessitate internal model fine-tuning, external model fine-tuning, or policy optimization over a defined state space. Implementing these methods can prove challenging due to the scarcity of high-quality training data or the lack of well-defined state space. Moreover, these agents do not possess certain qualities inherent to human decision-making processes, specifically the ability to learn from mistakes. Self-reflection allows humans to efficiently solve novel problems through a process of trial and error. Building on recent research, we propose Reflexion, an approach that endows an agent with dynamic memory and self-reflection capabilities to enhance its existing reasoning trace and task-specific action choice abilities. ...

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