Generalizing while preserving monotonicity in comparison-based preference learning models
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866908605248176128 |
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| author | Fageot, Julien Blanchard, Peva Bareilles, Gilles Hoang, Lê-Nguyên |
| author_facet | Fageot, Julien Blanchard, Peva Bareilles, Gilles Hoang, Lê-Nguyên |
| contents | If you tell a learning model that you prefer an alternative $a$ over another alternative $b$, then you probably expect the model to be monotone, that is, the valuation of $a$ increases, and that of $b$ decreases. Yet, perhaps surprisingly, many widely deployed comparison-based preference learning models, including large language models, fail to have this guarantee. Until now, the only comparison-based preference learning algorithms that were proved to be monotone are the Generalized Bradley-Terry models. Yet, these models are unable to generalize to uncompared data. In this paper, we advance the understanding of the set of models with generalization ability that are monotone. Namely, we propose a new class of Linear Generalized Bradley-Terry models with Diffusion Priors, and identify sufficient conditions on alternatives' embeddings that guarantee monotonicity. Our experiments show that this monotonicity is far from being a general guarantee, and that our new class of generalizing models improves accuracy, especially when the dataset is limited. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_08616 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generalizing while preserving monotonicity in comparison-based preference learning models Fageot, Julien Blanchard, Peva Bareilles, Gilles Hoang, Lê-Nguyên Statistics Theory Machine Learning If you tell a learning model that you prefer an alternative $a$ over another alternative $b$, then you probably expect the model to be monotone, that is, the valuation of $a$ increases, and that of $b$ decreases. Yet, perhaps surprisingly, many widely deployed comparison-based preference learning models, including large language models, fail to have this guarantee. Until now, the only comparison-based preference learning algorithms that were proved to be monotone are the Generalized Bradley-Terry models. Yet, these models are unable to generalize to uncompared data. In this paper, we advance the understanding of the set of models with generalization ability that are monotone. Namely, we propose a new class of Linear Generalized Bradley-Terry models with Diffusion Priors, and identify sufficient conditions on alternatives' embeddings that guarantee monotonicity. Our experiments show that this monotonicity is far from being a general guarantee, and that our new class of generalizing models improves accuracy, especially when the dataset is limited. |
| title | Generalizing while preserving monotonicity in comparison-based preference learning models |
| topic | Statistics Theory Machine Learning |
| url | https://arxiv.org/abs/2506.08616 |