@inproceedings{kumar-etal-2025-compo,
title = "{C}om{PO}: Community Preferences for Language Model Personalization",
author = "Kumar, Sachin and
Park, Chan Young and
Tsvetkov, Yulia and
Smith, Noah A. and
Hajishirzi, Hannaneh",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.419/",
doi = "10.18653/v1/2025.naacl-long.419",
pages = "8246--8279",
ISBN = "979-8-89176-189-6",
abstract = "Conventional algorithms for training language models (LMs) with human feedback rely on preferences that are assumed to account for an ``average'' user, disregarding subjectivity and finer-grained variations. Recent studies have raised concerns that aggregating such diverse and often contradictory human feedback to finetune models results in generic models that generate outputs not preferred by many user groups, as they tend to average out styles and norms. To address this issue, we draw inspiration from recommendation systems and propose ComPO, a method to personalize preference optimization in LMs by contextualizing the probability distribution of model outputs with the preference provider. Focusing on group-level preferences rather than individuals, we collect and release ComPRed, a question answering dataset with community-level preferences from Reddit. This dataset facilitates studying diversity in preferences without incurring privacy concerns associated with individual feedback. Our experiments reveal that conditioning language models on a community identifier (i.e., subreddit name) during preference tuning substantially enhances model performance. Conversely, replacing this context with random subreddit identifiers significantly diminishes performance, highlighting the effectiveness of our approach in tailoring responses to communities' preferences."
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<abstract>Conventional algorithms for training language models (LMs) with human feedback rely on preferences that are assumed to account for an “average” user, disregarding subjectivity and finer-grained variations. Recent studies have raised concerns that aggregating such diverse and often contradictory human feedback to finetune models results in generic models that generate outputs not preferred by many user groups, as they tend to average out styles and norms. To address this issue, we draw inspiration from recommendation systems and propose ComPO, a method to personalize preference optimization in LMs by contextualizing the probability distribution of model outputs with the preference provider. Focusing on group-level preferences rather than individuals, we collect and release ComPRed, a question answering dataset with community-level preferences from Reddit. This dataset facilitates studying diversity in preferences without incurring privacy concerns associated with individual feedback. Our experiments reveal that conditioning language models on a community identifier (i.e., subreddit name) during preference tuning substantially enhances model performance. Conversely, replacing this context with random subreddit identifiers significantly diminishes performance, highlighting the effectiveness of our approach in tailoring responses to communities’ preferences.</abstract>
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%0 Conference Proceedings
%T ComPO: Community Preferences for Language Model Personalization
%A Kumar, Sachin
%A Park, Chan Young
%A Tsvetkov, Yulia
%A Smith, Noah A.
%A Hajishirzi, Hannaneh
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-189-6
%F kumar-etal-2025-compo
%X Conventional algorithms for training language models (LMs) with human feedback rely on preferences that are assumed to account for an “average” user, disregarding subjectivity and finer-grained variations. Recent studies have raised concerns that aggregating such diverse and often contradictory human feedback to finetune models results in generic models that generate outputs not preferred by many user groups, as they tend to average out styles and norms. To address this issue, we draw inspiration from recommendation systems and propose ComPO, a method to personalize preference optimization in LMs by contextualizing the probability distribution of model outputs with the preference provider. Focusing on group-level preferences rather than individuals, we collect and release ComPRed, a question answering dataset with community-level preferences from Reddit. This dataset facilitates studying diversity in preferences without incurring privacy concerns associated with individual feedback. Our experiments reveal that conditioning language models on a community identifier (i.e., subreddit name) during preference tuning substantially enhances model performance. Conversely, replacing this context with random subreddit identifiers significantly diminishes performance, highlighting the effectiveness of our approach in tailoring responses to communities’ preferences.
%R 10.18653/v1/2025.naacl-long.419
%U https://aclanthology.org/2025.naacl-long.419/
%U https://doi.org/10.18653/v1/2025.naacl-long.419
%P 8246-8279
Markdown (Informal)
[ComPO: Community Preferences for Language Model Personalization](https://aclanthology.org/2025.naacl-long.419/) (Kumar et al., NAACL 2025)
ACL
- Sachin Kumar, Chan Young Park, Yulia Tsvetkov, Noah A. Smith, and Hannaneh Hajishirzi. 2025. ComPO: Community Preferences for Language Model Personalization. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 8246–8279, Albuquerque, New Mexico. Association for Computational Linguistics.