EpiPersona: Persona Projection and Episode Coupling for Pluralistic Preference Modeling

• URL: http://arxiv.org/abs/2603.28197v1
• Date: Mon, 30 Mar 2026 09:04:24 GMT
• Title: EpiPersona: Persona Projection and Episode Coupling for Pluralistic Preference Modeling
• Abstract summary: We introduce EpiPersona, a framework for explicit persona-episode coupling.<n>EpiPersona first projects noisy preference feedback into a low-dimensional persona space.<n>The inferred persona representation is then coupled with the current episode, enabling episode-aware preference prediction.
• Score: 27.99622587517494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pluralistic alignment is essential for adapting large language models (LLMs) to the diverse preferences of individuals and minority groups. However, existing approaches often mix stable personal traits with episode-specific factors, limiting their ability to generalize across episodes. To address this challenge, we introduce EpiPersona, a framework for explicit persona-episode coupling. EpiPersona first projects noisy preference feedback into a low-dimensional persona space, where similar personas are aggregated into shared discrete codes. This process separates enduring personal characteristics from situational signals without relying on predefined preference dimensions. The inferred persona representation is then coupled with the current episode, enabling episode-aware preference prediction. Extensive experiments show that EpiPersona consistently outperforms the baselines. It achieves notable performance gains in hard episodic-shift scenarios, while remaining effective with sparse preference data.

Related papers

• VIBE-Bench: Evaluating Personalized Large Language Models When Profiles Don't Mean Preferences [40.195544693657396]
  Existing benchmarks largely assume that preference can be retrieved from semantically related history.<n>We study an underexplored but practically important regime, profile-preference conceptual misalignment.<n>We introduce VIBE-Bench, a benchmark with two psychology-grounded tasks and 12,239 dialogues.
  arXiv  Detail & Related papers  (2026-09-01T08:45:28Z)
• Behaviorally Grounded User Profiles from the Wild for Personalized Alignment and Multi-Perspective Reasoning [19.59357421796823]
  We introduce profile behavioral grounding, a framework for extracting open-ended, high-fidelity user profiles directly from social media posts.<n>We evaluate these profiles across two paradigms: train-time personalization via supervised finetuning (SFT) and non-parametric test-time multi-perspective reasoning.<n>Our findings establish open-ended, behavior-derived profiles as a highly diverse and effective foundation for the next generation of personalized language systems.
  arXiv  Detail & Related papers  (2026-08-11T10:19:11Z)
• DuplexGen: Adaptive Synthesis of Human-AI Turn-Taking Dialogues [49.82200425774426]
  Turntaking is a central component of a full-adaptive interaction, yet current models apply regardless of context.<n>We present a framework for generating dialogues with scenario turntaking by calibrating predictions against a small set of human preference annotations.<n>These results show that human calibration, not prompting or prompt design alone, is what allows turn-taking synthesis to be scenario-specific.
  arXiv  Detail & Related papers  (2026-07-28T18:36:46Z)
• How Well Do Large Language Models Capture Human Personality? [38.97721735513693]
  Large language models (LLMs) are increasingly used to simulate human populations via persona prompting.<n>In this work, we formalize assumptions that richer persona descriptions improve behavioral fidelity, similarly sized attribute combinations are equally simulatable, and persona definitions generalize across tasks.<n>We identify a fundamental limitation we term persona manifold collapse, where increasingly expressive persona specifications lead to systematic contraction of representational and behavioral diversity.
  arXiv  Detail & Related papers  (2026-05-12T10:33:48Z)
• Response Time Enhances Alignment with Heterogeneous Preferences [49.69696266152175]
  We show that augmenting preference datasets with a simple, secondary signal can restore identifiability of the population's average preference.<n>Our results bring promises and open up new opportunities for future data-collection pipelines.
  arXiv  Detail & Related papers  (2026-05-07T22:05:23Z)
• PERMA: Benchmarking Personalized Memory Agents via Event-Driven Preference and Realistic Task Environments [72.02445514666428]
  We introduce PERMA, a benchmark designed to evaluate persona consistency over time beyond static preference recall.<n>PerMA consists of temporally ordered interaction events spanning multiple sessions and domains, with preference-related queries inserted over time.<n> Experiments demonstrate that by linking related interactions, advanced memory systems can extract more precise preferences and reduce token consumption.
  arXiv  Detail & Related papers  (2026-03-24T14:04:11Z)
• Synthetic Interaction Data for Scalable Personalization in Large Language Models [67.31884245564086]
  We introduce a high-fidelity synthetic data generation framework called PersonaGym.<n>Unlike prior work that treats personalization as static persona-preference pairs, PersonaGym models a dynamic preference process.<n>We release PersonaAtlas, a large-scale, high-quality, and diverse synthetic dataset of high-fidelity multi-turn personalized interaction trajectories.
  arXiv  Detail & Related papers  (2026-02-12T20:41:22Z)
• Few-shot Steerable Alignment: Adapting Rewards and LLM Policies with Neural Processes [50.544186914115045]
  Large language models (LLMs) are increasingly embedded in everyday applications.<n> Ensuring their alignment with the diverse preferences of individual users has become a critical challenge.<n>We present a novel framework for few-shot steerable alignment.
  arXiv  Detail & Related papers  (2024-12-18T16:14:59Z)
• ComPO: Community Preferences for Language Model Personalization [122.54846260663922]
  ComPO is a method to personalize preference optimization in language models. We collect and release ComPRed, a question answering dataset with community-level preferences from Reddit.
  arXiv  Detail & Related papers  (2024-10-21T14:02:40Z)
• Synergizing Unsupervised Episode Detection with LLMs for Large-Scale News Events [32.6146580586496]
  State-of-the-art automatic event detection struggles with interpretability and adaptability to evolving key events.<n>This paper introduces a novel task, episode detection, which identifies episodes within a news corpus of key event articles.<n>We introduce EpiMine, an unsupervised framework that identifies a key event's candidate episodes by leveraging natural episodic partitions in articles.
  arXiv  Detail & Related papers  (2024-08-09T05:26:31Z)
• PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences [6.398937923320069]
  We propose PAL, a framework to model human preference complementary to existing pretraining strategies. We show that PAL achieves competitive reward model accuracy compared to strong baselines.
  arXiv  Detail & Related papers  (2024-06-12T17:54:54Z)
• From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning [38.30983556062276]
  A major challenge in Natural Language Processing is obtaining annotated data for supervised learning. Crowdsourcing introduces issues related to the annotator's experience, consistency, and biases. This paper contributes an automatic and informed data selection architecture to build a small dataset for few-shot learning.
  arXiv  Detail & Related papers  (2024-01-24T04:57:32Z)
• AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model [69.12623428463573]
  AlignDiff is a novel framework to quantify human preferences, covering abstractness, and guide diffusion planning. It can accurately match user-customized behaviors and efficiently switch from one to another. We demonstrate its superior performance on preference matching, switching, and covering compared to other baselines.
  arXiv  Detail & Related papers  (2023-10-03T13:53:08Z)

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.