HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation

• URL: http://arxiv.org/abs/2505.00038v2
• Date: Mon, 19 May 2025 18:00:19 GMT
• Title: HyPerAlign: Interpretable Personalized LLM Alignment via Hypothesis Generation
• Authors: Cristina Garbacea, Chenhao Tan,
• Abstract summary: HyPerAlign is an interpretable and sample-efficient hypothesis-driven personalization approach for large language models.<n>We conduct experiments on two different personalization tasks, namely authorship attribution and deliberative alignment.<n>Results demonstrate the superiority of hypothesis-driven personalization compared to preference-based fine-tuning methods.
• Score: 24.67727411391369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alignment algorithms are widely used to align large language models (LLMs) to human users based on preference annotations. Typically these (often divergent) preferences are aggregated over a diverse set of users, resulting in fine-tuned models that are aligned to the ``average-user'' preference. Nevertheless, current models are used by individual users in very specific contexts and situations, emphasizing the need for user-dependent preference control. In this work we address the problem of personalizing LLM outputs to their users. We aim to generate customized responses tailored to specific individuals instead of generic outputs that emulate the collective voices of diverse populations. We propose HyPerAlign, an interpretable and sample-efficient hypothesis-driven personalization approach for LLM models. Given few-shot examples written by a particular user, we first infer hypotheses about their communication strategies, personality, and writing style, then prompt LLM models with these hypotheses and user-specific attributes to generate customized outputs. We conduct experiments on two different personalization tasks, namely authorship attribution and deliberative alignment, with datasets from diverse domains (news articles, blog posts, emails, jailbreaking benchmarks). Results demonstrate the superiority of hypothesis-driven LLM personalization compared to preference-based fine-tuning methods. For authorship attribution, HyPerAlign generations have consistently high win-rates (commonly $> 90\%$) against state-of-the-art preference fine-tuning approaches across diverse user profiles and LLM models. For deliberative alignment, the helpfulness of LLM models is improved by up to $70\%$ on average. Overall, HyPerAlign represents an interpretable and sample-efficient strategy for the personalization of LLM models to individual users.

Related papers

• LoRe: Personalizing LLMs via Low-Rank Reward Modeling [47.12507639759984]
  We introduce a novel framework that leverages low-rank preference modeling to efficiently learn and generalize user-specific reward functions.<n>We validate our method on multiple preference datasets, demonstrating superior generalization to unseen users and improved accuracy in preference prediction tasks.
  arXiv  Detail & Related papers  (2025-04-20T01:16:24Z)
• Measuring What Makes You Unique: Difference-Aware User Modeling for Enhancing LLM Personalization [68.79814761867314]
  We propose Difference-aware Personalization Learning (DPL) to enhance Large Language Models (LLMs) personalization.<n>DPL strategically selects representative users for comparison and establishes a structured standard to extract task-relevant differences.<n>Experiments on real-world datasets demonstrate that DPL significantly enhances LLM personalization.
  arXiv  Detail & Related papers  (2025-03-04T09:53:26Z)
• Personalized Preference Fine-tuning of Diffusion Models [75.22218338096316]
  We introduce PPD, a multi-reward optimization objective that aligns diffusion models with personalized preferences.<n>With PPD, a diffusion model learns the individual preferences of a population of users in a few-shot way.<n>Our approach achieves an average win rate of 76% over Stable Cascade, generating images that more accurately reflect specific user preferences.
  arXiv  Detail & Related papers  (2025-01-11T22:38:41Z)
• 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)
• Aligning LLMs with Individual Preferences via Interaction [51.72200436159636]
  We train large language models (LLMs) that can ''interact to align''<n>We develop a multi-turn preference dataset containing 3K+ multi-turn conversations in tree structures.<n>For evaluation, we establish the ALOE benchmark, consisting of 100 carefully selected examples and well-designed metrics to measure the customized alignment performance during conversations.
  arXiv  Detail & Related papers  (2024-10-04T17:48:29Z)
• PersonalLLM: Tailoring LLMs to Individual Preferences [11.717169516971856]
  We present a public benchmark, PersonalLLM, focusing on adapting LLMs to provide maximal benefits for a particular user.<n>We curate open-ended prompts paired with many high-quality answers over which users would be expected to display heterogeneous latent preferences.<n>Our dataset and generated personalities offer an innovative testbed for developing personalization algorithms.
  arXiv  Detail & Related papers  (2024-09-30T13:55:42Z)
• LLMs + Persona-Plug = Personalized LLMs [41.60364110693824]
  Personalization plays a critical role in numerous language tasks and applications, since users with the same requirements may prefer diverse outputs based on their individual interests. This has led to the development of various personalized approaches aimed at adapting large language models (LLMs) to generate customized outputs aligned with user preferences. We propose a novel personalized LLM model, ours. It constructs a user-specific embedding for each individual by modeling all her historical contexts through a lightweight plug-in user embedder module.
  arXiv  Detail & Related papers  (2024-09-18T11:54:45Z)
• PMG : Personalized Multimodal Generation with Large Language Models [20.778869086174137]
  This paper proposes the first method for personalized multimodal generation using large language models (LLMs) It showcases its applications and validates its performance via an extensive experimental study on two datasets. PMG has a significant improvement on personalization for up to 8% in terms of LPIPS while retaining the accuracy of generation.
  arXiv  Detail & Related papers  (2024-04-07T03:05:57Z)
• Personalized Soups: Personalized Large Language Model Alignment via Post-hoc Parameter Merging [148.77027765872006]
  We study Reinforcement Learning from Personalized Human Feedback (RLPHF) problem. LLMs are aligned to multiple preferences by modeling alignment as a Multi-Objective Reinforcement Learning (MORL) problem. We show that we can achieve personalized alignment by decomposing preferences into multiple dimensions.
  arXiv  Detail & Related papers  (2023-10-17T20:22:13Z)

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.