@inproceedings{jang-silavong-2025-instajudge,
title = "{I}nsta{J}udge: Aligning Judgment Bias of {LLM}-as-Judge with Humans in Industry Applications",
author = "Jang, Myeongjun Erik and
Silavong, Fran",
editor = "Potdar, Saloni and
Rojas-Barahona, Lina and
Montella, Sebastien",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = nov,
year = "2025",
address = "Suzhou (China)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-industry.82/",
pages = "1158--1172",
ISBN = "979-8-89176-333-3",
abstract = "Automated evaluation using LLM-as-Judge offers significant practical benefits for industrial applications. However, the commonly recognized misalignment of judgment biases between humans and LLM-as-Judge hinders its usage in real-world businesses. Although preference-finetuning could be a potential solution, it is often impractical for industrial use-cases due to the scarcity of business-specific data and the infeasibility of applying it to closed models. In this paper, we propose InstaJudge, an LLM-as-Judge library that improves alignments of judgment biases through automatic prompt optimization (APO). Our library not only integrates recent APO methods within a unified framework but also introduces a novel APO approach called distribution-preserving few-shot sampling (DPFS). Experimental results verify demonstrate DPFS significantly outperforms existing LLM-as-Judge libraries, like DeepEval, and APO methods by a large margin, while being more cost efficient."
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%0 Conference Proceedings
%T InstaJudge: Aligning Judgment Bias of LLM-as-Judge with Humans in Industry Applications
%A Jang, Myeongjun Erik
%A Silavong, Fran
%Y Potdar, Saloni
%Y Rojas-Barahona, Lina
%Y Montella, Sebastien
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou (China)
%@ 979-8-89176-333-3
%F jang-silavong-2025-instajudge
%X Automated evaluation using LLM-as-Judge offers significant practical benefits for industrial applications. However, the commonly recognized misalignment of judgment biases between humans and LLM-as-Judge hinders its usage in real-world businesses. Although preference-finetuning could be a potential solution, it is often impractical for industrial use-cases due to the scarcity of business-specific data and the infeasibility of applying it to closed models. In this paper, we propose InstaJudge, an LLM-as-Judge library that improves alignments of judgment biases through automatic prompt optimization (APO). Our library not only integrates recent APO methods within a unified framework but also introduces a novel APO approach called distribution-preserving few-shot sampling (DPFS). Experimental results verify demonstrate DPFS significantly outperforms existing LLM-as-Judge libraries, like DeepEval, and APO methods by a large margin, while being more cost efficient.
%U https://aclanthology.org/2025.emnlp-industry.82/
%P 1158-1172
Markdown (Informal)
[InstaJudge: Aligning Judgment Bias of LLM-as-Judge with Humans in Industry Applications](https://aclanthology.org/2025.emnlp-industry.82/) (Jang & Silavong, EMNLP 2025)
ACL