HPSS : Heuristic Prompting Strategy Search for LLM Evaluators (2025)

Abstract: Since the adoption of large language models (LLMs) for text evaluation has become increasingly prevalent in the field of natural language processing (NLP), a series of existing works attempt to optimize the prompts for LLM evaluators to improve their alignment with human judgment. However, their efforts are limited to optimizing individual factors of evaluation prompts, such as evaluation criteria or output formats, neglecting the combinatorial impact of multiple factors, which leads to insufficient optimization of the evaluation pipeline. Nevertheless, identifying well-behaved prompting strategies for adjusting multiple factors requires extensive enumeration. To this end, we comprehensively integrate 8 key factors for evaluation prompts and propose a novel automatic prompting strategy optimization method called Heuristic Prompting Strategy Search (HPSS). Inspired by the genetic algorithm, HPSS conducts an iterative search to find well-behaved prompting strategies for LLM evaluators. A heuristic function is employed to guide the search process, enhancing the performance of our algorithm. Extensive experiments across four evaluation tasks demonstrate the effectiveness of HPSS, consistently outperforming both human-designed evaluation prompts and existing automatic prompt optimization methods. Our code is available athttps://github.com/thu-coai/HPSS.

## introduction evaluation is a long-standing critical and challenging task in nlp @xcite . recently, with the advent of advanced large language models (llms) such as gpt-4, llm-based evaluators have been widely used for various natural language generation tasks (wang et al., 2023a; liu et al., 2023; zheng et al., † work done when this author interned at zhipu ai. ## conclusion in this work, we integrate 8 key factors for the prompting strategy of llm evaluators and propose hpss, a heuristic search method to automatically optimize the prompting strategy for adjusting these factors. hpss leverages the expected performance advantages of each value as the heuristic function to guide the search process. extensive experiments on four nlg evaluation tasks demonstrate the superiority of hpss, yielding consistent performance gains over both human-designed evaluation prompts and automatic prompt optimization methods. additionally, we validate the generalizability of prompting strategies found by hpss and analyze the characteristics of these factors. our experimental results may provide new insights into future prompt design for llm evaluators.