@inproceedings{mondal-etal-2025-group,
title = "Group Preference Alignment: Customizing {LLM} Responses from In-Situ Conversations Only When Needed",
author = "Mondal, Ishani and
Stokes, Jack W. and
Jauhar, Sujay Kumar and
Yang, Longqi and
Wan, Mengting and
Xu, Xiaofeng and
Song, Xia and
Boyd-Graber, Jordan Lee and
Neville, Jennifer",
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.56/",
pages = "825--849",
ISBN = "979-8-89176-333-3",
abstract = "LLMs often fail to meet specialized needs of distinct user groups due to their one-size-fits-all approach, and there is limited understanding of what personalization each group expects.To address this, we propose GPA a group-aware personalization framework that captures context-specific preference variations and steers LLMs accordingly.Our approach involves: (1) Group-Aware Preference Extraction, which distills divergent preferences from real-world conversation logs into interpretable rubrics, and (2) Tailored Response Generation, using (a) GPA-CT, which adapts responses using learnt rubrics, and (b) GPA-FT, which finetunes models using rubric-guided synthetic data.Automatic and Human evaluations confirm that GPA improves group alignment without compromising perfomance on standard instruction-following benchmarks."
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<abstract>LLMs often fail to meet specialized needs of distinct user groups due to their one-size-fits-all approach, and there is limited understanding of what personalization each group expects.To address this, we propose GPA a group-aware personalization framework that captures context-specific preference variations and steers LLMs accordingly.Our approach involves: (1) Group-Aware Preference Extraction, which distills divergent preferences from real-world conversation logs into interpretable rubrics, and (2) Tailored Response Generation, using (a) GPA-CT, which adapts responses using learnt rubrics, and (b) GPA-FT, which finetunes models using rubric-guided synthetic data.Automatic and Human evaluations confirm that GPA improves group alignment without compromising perfomance on standard instruction-following benchmarks.</abstract>
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%0 Conference Proceedings
%T Group Preference Alignment: Customizing LLM Responses from In-Situ Conversations Only When Needed
%A Mondal, Ishani
%A Stokes, Jack W.
%A Jauhar, Sujay Kumar
%A Yang, Longqi
%A Wan, Mengting
%A Xu, Xiaofeng
%A Song, Xia
%A Boyd-Graber, Jordan Lee
%A Neville, Jennifer
%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 mondal-etal-2025-group
%X LLMs often fail to meet specialized needs of distinct user groups due to their one-size-fits-all approach, and there is limited understanding of what personalization each group expects.To address this, we propose GPA a group-aware personalization framework that captures context-specific preference variations and steers LLMs accordingly.Our approach involves: (1) Group-Aware Preference Extraction, which distills divergent preferences from real-world conversation logs into interpretable rubrics, and (2) Tailored Response Generation, using (a) GPA-CT, which adapts responses using learnt rubrics, and (b) GPA-FT, which finetunes models using rubric-guided synthetic data.Automatic and Human evaluations confirm that GPA improves group alignment without compromising perfomance on standard instruction-following benchmarks.
%U https://aclanthology.org/2025.emnlp-industry.56/
%P 825-849
Markdown (Informal)
[Group Preference Alignment: Customizing LLM Responses from In-Situ Conversations Only When Needed](https://aclanthology.org/2025.emnlp-industry.56/) (Mondal et al., EMNLP 2025)
ACL
- Ishani Mondal, Jack W. Stokes, Sujay Kumar Jauhar, Longqi Yang, Mengting Wan, Xiaofeng Xu, Xia Song, Jordan Lee Boyd-Graber, and Jennifer Neville. 2025. Group Preference Alignment: Customizing LLM Responses from In-Situ Conversations Only When Needed. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 825–849, Suzhou (China). Association for Computational Linguistics.