arXiv：cs.LG· Yujie Chen, Antik Chakraborty, Anindya Bhadra·· 4 小时前AI 评分32
协变量相关的多元有序偏好联合建模及其与比较模型的关系
Covariate-dependent Joint Modeling of Multivariate Ordinal Preferences and Its Connections with Comparison Models
AI 导读
研究提出协变量相关的 consecutive ratio Markov random field 模型，对多元有序偏好数据进行联合建模，而非逐个属性单独处理或转换为成对、列表式胜负比较。该模型在受限条件下可导出 Bradley-Terry 和 Plackett-Luce 等比较模型，并证明联合建模能改进比较效果。作者还开发了在归一化常数不可解情况下仍适用的最大似然推断方法。
正文
Abstract:Multivariate ordinal data along with covariates are commonly collected in problems ranging from alignment of language models with human preferences, as well as in recommender systems. For example, data sets such as MovieLens contain several movies rated on a scale 1--5 by human users, along with their demographic information such as age or gender. Similarly, data sets such as HelpSteer collect human feedback on several attributes such as "helpfulness" or "verbosity" of LLM response on an ordinal scale, with covariates depending on the LLM prompt--response pairs. Unfortunately, the standard approaches for modeling these data (a) look at the attributes individually rather than jointly, and (b) often convert the data into pairwise or list-wise win--loss comparisons for fitting models such as Bradley--Terry and Plackett--Luce. Both of these lead to a coarsening of what is actually observed, which we address via a joint covariate-dependent consecutive ratio Markov random field model. We also show pairwise or listwise comparison models are obtained under restrictions of our joint model, and that joint modeling improves comparisons. We also develop a maximum likelihood inference procedure even in the presence of an intractable normalizer.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG); Methodology (stat.ME) |
| Cite as: | arXiv:2610.09070 [stat.ML] |
| (or arXiv:2610.09070v1 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09070 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Antik Chakraborty [view email]
[v1]
Tue, 6 Oct 2026 20:15:43 UTC (59 KB)
来源：arXiv：cs.LG · arxiv.org