arXiv AI
Learning Transferable Latent User Preferences for Human-Aligned Decision Making
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 21
From Weights to Words: Expressing and Editing Preference Model Inferences in Natural Language
arXiv:2607. 16232v1 Announce Type: cross Abstract: The growing use of statistical learning algorithms to infer human preferences from high-dimensional choice data runs up against a fundamental challenge: choice alternatives typically differ in many ways simultaneously, so it is generally unclear which factors actually drove an observed decision and should be credited as preferences.
arXiv AI
Sep 1
Towards Natural Personalization: Evaluating Long-Horizon Preference Following in Personalized User-LLM Interactions
arXiv:2603.04191v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly serving as personal assistants, where users may share individual preferences over extended interactio...
arXiv Machine Learning
Sep 15
Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States
arXiv:2609.15972v1 Announce Type: cross Abstract: As language models become more capable, long-term collaboration in learning, reasoning, and decision-making calls for a deeper understanding of the p...
arXiv AI
Aug 11
Learning Preference Adaptation for Large Language Model Personalization via Verbal Reinforcement Learning
arXiv:2608. 09507v1 Announce Type: cross Abstract: Natural language user preferences provide an interpretable interface for LLM personalization.
arXiv AI
Jun 4
Aligning Deep Implicit Preferences by Learning to Reason Defensively
arXiv:2510. 11194v3 Announce Type: replace Abstract: Personalized alignment is crucial for enabling Large Language Models (LLMs) to engage effectively in user-centric interactions.
arXiv AI
Sep 30
PADM\'E: Preference Alignment Data Synthesis for Meta-Evaluation of LM Agent Evaluators
PADM'E is a method for synthesizing preference‑aligned data to meta‑evaluate language‑model (LM) evaluators of agentic behaviors. It reframes meta‑evaluation as a preference judgment problem, generating criterion‑based data with small LMs and no human involvement. In a prototype, PADM'E produced 1,000 samples across four domains and three criteria, and human validation showed agreement with human judgment rising from 73% to 85% compared to a naive baseline.