@inproceedings{wen-etal-2025-hpss,
title = "{HPSS}: Heuristic Prompting Strategy Search for {LLM} Evaluators",
author = "Wen, Bosi and
Ke, Pei and
Sun, Yufei and
Wang, Cunxiang and
Gu, Xiaotao and
Zhou, Jinfeng and
Tang, Jie and
Wang, Hongning and
Huang, Minlie",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1282/",
doi = "10.18653/v1/2025.findings-acl.1282",
pages = "24974--25007",
ISBN = "979-8-89176-256-5",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T HPSS: Heuristic Prompting Strategy Search for LLM Evaluators
%A Wen, Bosi
%A Ke, Pei
%A Sun, Yufei
%A Wang, Cunxiang
%A Gu, Xiaotao
%A Zhou, Jinfeng
%A Tang, Jie
%A Wang, Hongning
%A Huang, Minlie
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wen-etal-2025-hpss
%X 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.
%R 10.18653/v1/2025.findings-acl.1282
%U https://aclanthology.org/2025.findings-acl.1282/
%U https://doi.org/10.18653/v1/2025.findings-acl.1282
%P 24974-25007
Markdown (Informal)
[HPSS: Heuristic Prompting Strategy Search for LLM Evaluators](https://aclanthology.org/2025.findings-acl.1282/) (Wen et al., Findings 2025)
ACL
- Bosi Wen, Pei Ke, Yufei Sun, Cunxiang Wang, Xiaotao Gu, Jinfeng Zhou, Jie Tang, Hongning Wang, and Minlie Huang. 2025. HPSS: Heuristic Prompting Strategy Search for LLM Evaluators. In Findings of the Association for Computational Linguistics: ACL 2025, pages 24974–25007, Vienna, Austria. Association for Computational Linguistics.