Preference Optimization with Multi-Sample Comparisons

• URL: http://arxiv.org/abs/2410.12138v1
• Date: Wed, 16 Oct 2024 00:59:19 GMT
• Title: Preference Optimization with Multi-Sample Comparisons
• Authors: Chaoqi Wang, Zhuokai Zhao, Chen Zhu, Karthik Abinav Sankararaman, Michal Valko, Xuefei Cao, Zhaorun Chen, Madian Khabsa, Yuxin Chen, Hao Ma, Sinong Wang,
• Abstract summary: We introduce a novel approach that extends post-training to include multi-sample comparisons. These approaches fail to capture critical characteristics such as generative diversity and bias. We demonstrate that multi-sample comparison is more effective in optimizing collective characteristics than single-sample comparison.
• Score: 53.02717574375549
• License:
• Abstract: Recent advancements in generative models, particularly large language models (LLMs) and diffusion models, have been driven by extensive pretraining on large datasets followed by post-training. However, current post-training methods such as reinforcement learning from human feedback (RLHF) and direct alignment from preference methods (DAP) primarily utilize single-sample comparisons. These approaches often fail to capture critical characteristics such as generative diversity and bias, which are more accurately assessed through multiple samples. To address these limitations, we introduce a novel approach that extends post-training to include multi-sample comparisons. To achieve this, we propose Multi-sample Direct Preference Optimization (mDPO) and Multi-sample Identity Preference Optimization (mIPO). These methods improve traditional DAP methods by focusing on group-wise characteristics. Empirically, we demonstrate that multi-sample comparison is more effective in optimizing collective characteristics~(e.g., diversity and bias) for generative models than single-sample comparison. Additionally, our findings suggest that multi-sample comparisons provide a more robust optimization framework, particularly for dataset with label noise.

Related papers

• Calibrated Multi-Preference Optimization for Aligning Diffusion Models [92.90660301195396]
  Calibrated Preference Optimization (CaPO) is a novel method to align text-to-image (T2I) diffusion models. CaPO incorporates the general preference from multiple reward models without human annotated data. Experimental results show that CaPO consistently outperforms prior methods.
  arXiv  Detail & Related papers  (2025-02-04T18:59:23Z)
• Clear Preferences Leave Traces: Reference Model-Guided Sampling for Preference Learning [59.11519451499754]
  Direct Preference Optimization (DPO) has emerged as a de-facto approach for aligning language models with human preferences. Recent work has shown DPO's effectiveness relies on training data quality. We discover that reference model probability space naturally detects high-quality training samples.
  arXiv  Detail & Related papers  (2025-01-25T07:21:50Z)
• Personalized Preference Fine-tuning of Diffusion Models [75.22218338096316]
  We introduce PPD, a multi-reward optimization objective that aligns diffusion models with personalized preferences. With PPD, a diffusion model learns the individual preferences of a population of users in a few-shot way. 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)
• Multi-Reference Preference Optimization for Large Language Models [56.84730239046117]
  We introduce a novel closed-form formulation for direct preference optimization using multiple reference models. The resulting algorithm, Multi-Reference Preference Optimization (MRPO), leverages broader prior knowledge from diverse reference models. Our experiments demonstrate that LLMs finetuned with MRPO generalize better in various preference data, regardless of data scarcity or abundance.
  arXiv  Detail & Related papers  (2024-05-26T00:29:04Z)
• Direct Preference Optimization With Unobserved Preference Heterogeneity [16.91835461818937]
  This paper presents a new method to align generative models with varied human preferences. We propose an Expectation-Maximization adaptation to DPO, generating a mixture of models based on latent preference types of the annotators. Our algorithms leverage the simplicity of DPO while accommodating diverse preferences.
  arXiv  Detail & Related papers  (2024-05-23T21:25:20Z)
• Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning [47.02160072880698]
  We introduce a self-evolving mechanism that allows the model itself to actively sample subsets that are equally or even more effective. The key to our data sampling technique lies in the enhancement of diversity in the chosen subsets. Extensive experiments across three datasets and benchmarks demonstrate the effectiveness of DiverseEvol.
  arXiv  Detail & Related papers  (2023-11-14T14:10:40Z)
• Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization [73.45742420178196]
  We propose a novel multimodal response generation framework with exemplar augmentation and curriculum optimization. Our model achieves significant improvements compared to strong baselines in terms of diversity and relevance.
  arXiv  Detail & Related papers  (2020-04-26T16:29:06Z)

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.