@inproceedings{tang-etal-2026-learning,
title = "Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple {LLM} Judges",
author = "Tang, Yuqi and
Feng, Kehua and
Wang, Yunfeng and
Chen, Zhiwen and
lv, Chengfei and
Yu, Gang and
Zhang, Qiang and
Ding, Keyan and
Chen, Huajun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.188/",
pages = "4304--4327",
ISBN = "979-8-89176-459-0",
abstract = "Evaluating the conversational abilities of large language models (LLMs) remains a challenging task. Current mainstream approaches primarily rely on the ``LLM-as-a-judge'' paradigm, where an LLM is prompted to serve as an evaluator to assess dialogue quality. However, such methods often suffer from various biases, which undermine the reliability and consistency of the evaluation results. To mitigate these biases, recent methods employ multiple LLMs as judges and aggregate their judgments to select the optimal assessment. Although effective, this multi-judge approach incurs significant computational overhead during inference. In this paper, we propose an efficient multi-turn dialogue evaluator that captures the collective wisdom of multiple LLM judges by aggregating their preference knowledge into a single model. Our approach preserves the advantages of diverse multi-judge feedback while drastically reducing the evaluation cost, enabling fast and flexible dialogue quality assessment. Extensive experiments on seven single rating and pairwise comparison dialogue evaluation benchmarks demonstrate that our method outperforms existing baselines across diverse scenarios, showcasing its efficiency and robustness."
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<abstract>Evaluating the conversational abilities of large language models (LLMs) remains a challenging task. Current mainstream approaches primarily rely on the “LLM-as-a-judge” paradigm, where an LLM is prompted to serve as an evaluator to assess dialogue quality. However, such methods often suffer from various biases, which undermine the reliability and consistency of the evaluation results. To mitigate these biases, recent methods employ multiple LLMs as judges and aggregate their judgments to select the optimal assessment. Although effective, this multi-judge approach incurs significant computational overhead during inference. In this paper, we propose an efficient multi-turn dialogue evaluator that captures the collective wisdom of multiple LLM judges by aggregating their preference knowledge into a single model. Our approach preserves the advantages of diverse multi-judge feedback while drastically reducing the evaluation cost, enabling fast and flexible dialogue quality assessment. Extensive experiments on seven single rating and pairwise comparison dialogue evaluation benchmarks demonstrate that our method outperforms existing baselines across diverse scenarios, showcasing its efficiency and robustness.</abstract>
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%0 Conference Proceedings
%T Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple LLM Judges
%A Tang, Yuqi
%A Feng, Kehua
%A Wang, Yunfeng
%A Chen, Zhiwen
%A lv, Chengfei
%A Yu, Gang
%A Zhang, Qiang
%A Ding, Keyan
%A Chen, Huajun
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F tang-etal-2026-learning
%X Evaluating the conversational abilities of large language models (LLMs) remains a challenging task. Current mainstream approaches primarily rely on the “LLM-as-a-judge” paradigm, where an LLM is prompted to serve as an evaluator to assess dialogue quality. However, such methods often suffer from various biases, which undermine the reliability and consistency of the evaluation results. To mitigate these biases, recent methods employ multiple LLMs as judges and aggregate their judgments to select the optimal assessment. Although effective, this multi-judge approach incurs significant computational overhead during inference. In this paper, we propose an efficient multi-turn dialogue evaluator that captures the collective wisdom of multiple LLM judges by aggregating their preference knowledge into a single model. Our approach preserves the advantages of diverse multi-judge feedback while drastically reducing the evaluation cost, enabling fast and flexible dialogue quality assessment. Extensive experiments on seven single rating and pairwise comparison dialogue evaluation benchmarks demonstrate that our method outperforms existing baselines across diverse scenarios, showcasing its efficiency and robustness.
%U https://aclanthology.org/2026.emnlp-main.188/
%P 4304-4327
Markdown (Informal)
[Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple LLM Judges](https://aclanthology.org/2026.emnlp-main.188/) (Tang et al., EMNLP 2026)
ACL
- Yuqi Tang, Kehua Feng, Yunfeng Wang, Zhiwen Chen, Chengfei lv, Gang Yu, Qiang Zhang, Keyan Ding, and Huajun Chen. 2026. Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple LLM Judges. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 4304–4327, Budapest, Hungary. Association for Computational Linguistics.