@inproceedings{
nie2023importance,
title={Importance of Directional Feedback for {LLM}-based Optimizers},
author={Allen Nie and Ching-An Cheng and Andrey Kolobov and Adith Swaminathan},
booktitle={NeurIPS 2023 Foundation Models for Decision Making Workshop},
year={2023},
url={https://openreview.net/forum?id=QW4eGh5GT3}
}
We study the potential of using large language models (LLMs) as an interactive 
optimizer for solving maximization problems on 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.
Importance of Directional Feedback for LLM-based Optimizers
Allen Nie Ching-An Cheng Andrey Kolobov Adith Swaminathan