@inproceedings{nie2023importance,
author = {Nie, Allen and Cheng, Ching-An and Kolobov, Andrey and Swaminathan, Adith},
title = {Importance of Directional Feedback for LLM-based Optimizers},
booktitle = {NeurIPS 2023 Foundation Models for Decision Making Workshop},
year = {2023},
month = {December},
abstract = {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.},
url = {http://approjects.co.za/?big=en-us/research/publication/importance-of-directional-feedback-for-llm-based-optimizers/},
}