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arXiv · 2405.16434

The Importance of Directional Feedback for LLM-based Optimizers

Allen Nie·Ching-An Cheng·Andrey Kolobov·Adith Swaminathan
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Abstract

We study the potential of using large language models (LLMs) as an interactive optimizer for solving maximization problems in a text space using natural language and numerical feedback. Inspired by the classical optimization literature, we classify the natural language feedback into directional and non-directional, where the former is a generalization of the first-order feedback to the natural language space. We find that LLMs are especially capable of optimization when they are provided with {directional feedback}. Based on this insight, we design a new LLM-based optimizer that synthesizes directional feedback from the historical optimization trace to achieve reliable improvement over iterations. Empirically, we show our LLM-based optimizer is more stable and efficient in solving optimization problems, from maximizing mathematical functions to optimizing prompts for writing poems, compared with existing techniques.

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Allen Nie, Ching-An Cheng, Andrey Kolobov, Adith Swaminathan. 2024-05-26. The Importance of Directional Feedback for LLM-based Optimizers. https://arxiv.org/abs/2405.16434

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