Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization

• URL: http://arxiv.org/abs/2604.00997v1
• Date: Wed, 01 Apr 2026 14:58:51 GMT
• Title: Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization
• Abstract summary: We introduce Variational Reward Factorization (VRF), an uncertainty-aware framework that represents each user's preferences as a variational distribution in a shared preference space.<n>VRF infers user distributions via a variational encoder, derives weights through Wasserstein distance matching with shared probabilistic bases, and downweights uncertain estimates through a variance-attenuated loss.
• Score: 32.29307367283854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared basis functions and user-specific weights. Yet, existing methods estimate user weights from scarce data in isolation and as deterministic points, leading to inaccurate and unreliable inference. We introduce Variational Reward Factorization (VRF), an uncertainty-aware framework that represents each user's preferences as a variational distribution in a shared preference space. VRF infers user distributions via a variational encoder, derives weights through Wasserstein distance matching with shared probabilistic bases, and downweights uncertain estimates through a variance-attenuated loss. On three benchmarks, VRF outperforms all baselines across seen and unseen users, few-shot scenarios, and varying uncertainty levels, with gains extending to downstream alignment.

Related papers

• Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations [55.76952683833966]
  This paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC) to reduce confidence set size when data may be corrupted.<n>Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size.
  arXiv  Detail & Related papers  (2026-07-29T05:16:59Z)
• LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy [1.3254304182988286]
  We propose Adaptive Conformal Semantic Entropy (ACSE), a method for estimating prompt-level uncertainty.<n>Our uncertainty scoring function is based on clustering semantic entropy of multiple diverse responses to the same prompt.<n>Our approach consistently outperforms state-of-the-art uncertainty quantification baselines.
  arXiv  Detail & Related papers  (2026-05-05T20:56:11Z)
• Uncertainty Estimation using Variance-Gated Distributions [0.6340400318304492]
  We propose an intuitive framework for uncertainty estimation and decomposition based on the signal-to-noise ratio of class probability distributions.<n>We introduce a variance-gated measure that scales predictions by a confidence factor derived from ensembles.
  arXiv  Detail & Related papers  (2025-09-07T16:19:21Z)
• FIVA: Federated Inverse Variance Averaging for Universal CT Segmentation with Uncertainty Estimation [4.544160712377809]
  This work presents a novel federated learning approach to achieve universal segmentation across diverse abdominal CT datasets.<n>The proposed method quantifies prediction uncertainty by propagating the uncertainty from the model weights.<n> Experimental evaluations demonstrate the effectiveness of this approach in improving the quality of federated aggregation and uncertainty-weighted inference.
  arXiv  Detail & Related papers  (2025-08-08T11:34:01Z)
• Uncertainty-driven Embedding Convolution [16.523816971857787]
  We propose Uncertainty-driven Embedding Convolution (UEC)<n>UEC transforms deterministic embeddings into probabilistic ones in a post-hoc manner.<n>It then computes adaptive ensemble weights based on embedding uncertainty.
  arXiv  Detail & Related papers  (2025-07-28T11:15:25Z)
• TRUST: Test-time Resource Utilization for Superior Trustworthiness [15.031121920821109]
  We propose a novel test-time optimization method that accounts for the impact of such noise to produce more reliable confidence estimates.<n>This score defines a monotonic subset-selection function, where population accuracy consistently increases as samples with lower scores are removed.
  arXiv  Detail & Related papers  (2025-06-06T12:52:32Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)
• Likelihood Ratio Confidence Sets for Sequential Decision Making [51.66638486226482]
  We revisit the likelihood-based inference principle and propose to use likelihood ratios to construct valid confidence sequences. Our method is especially suitable for problems with well-specified likelihoods. We show how to provably choose the best sequence of estimators and shed light on connections to online convex optimization.
  arXiv  Detail & Related papers  (2023-11-08T00:10:21Z)
• Quantification of Predictive Uncertainty via Inference-Time Sampling [57.749601811982096]
  We propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions.
  arXiv  Detail & Related papers  (2023-08-03T12:43:21Z)
• Robust Outlier Rejection for 3D Registration with Variational Bayes [70.98659381852787]
  We develop a novel variational non-local network-based outlier rejection framework for robust alignment. We propose a voting-based inlier searching strategy to cluster the high-quality hypothetical inliers for transformation estimation.
  arXiv  Detail & Related papers  (2023-04-04T03:48:56Z)
• Triplet Losses-based Matrix Factorization for Robust Recommendations [0.76146285961466]
  We propose using multiple triplet losses terms to extract meaningful representations of users and items. We empirically evaluate the soundness of such representations through several "bias-aware" evaluation metrics.
  arXiv  Detail & Related papers  (2022-10-21T16:44:59Z)
• Uncertainty-guided Source-free Domain Adaptation [77.3844160723014]
  Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. We propose quantifying the uncertainty in the source model predictions and utilizing it to guide the target adaptation.
  arXiv  Detail & Related papers  (2022-08-16T08:03:30Z)
• Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness [61.827054365139645]
  Variational Autoencoder (VAE) approximates the posterior of latent variables based on amortized variational inference. We propose an alternative model, DU-VAE, for learning a more Diverse and less Uncertain latent space.
  arXiv  Detail & Related papers  (2021-10-24T07:58:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.