3 papers
cs.LG2026
TextBFGS: A Case-Based Reasoning Approach to Code Optimization via Error-Operator Retrieval
Zizheng Zhang, Yuyang Liao, Chen Chen +8
Iterative code generation with Large Language Models (LLMs) can be viewed as an optimization process guided by textual feedback. However, existing LLM self-correction methods predo…
cs.CL2024
AMPO: Automatic Multi-Branched Prompt Optimization
Sheng Yang, Yurong Wu, Yan Gao +10
Prompt engineering is very important to enhance the performance of large language models (LLMs). When dealing with complex issues, prompt engineers tend to distill multiple pattern…
cs.CL2024
StraGo: Harnessing Strategic Guidance for Prompt Optimization
Yurong Wu, Yan Gao, Bin Benjamin Zhu +6
Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications. While existing prompt optimization methods improve prompt…