Multi-Response Preference Optimization with Augmented Ranking Dataset

• URL: http://arxiv.org/abs/2412.07812v1
• Date: Tue, 10 Dec 2024 05:45:36 GMT
• Title: Multi-Response Preference Optimization with Augmented Ranking Dataset
• Authors: Hansle Gwon, Imjin Ahn, Young-Hak Kim, Sanghyun Park, Tae Joon Jun,
• Abstract summary: Preference optimization has played a significant role in improving the performance of Large Language Models.<n>We propose a novel approach to augment Preference Optimization datasets.<n>We also introduce a Multi-response-based Preference Optimization training method.
• Score: 2.8973004951877357
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advancements in Large Language Models (LLMs) have been remarkable, with new models consistently surpassing their predecessors. These advancements are underpinned by extensive research on various training mechanisms. Among these, Preference Optimization has played a significant role in improving the performance of LLMs by incorporating human preferences into the training process. However, constructing preference optimization datasets is challenging and the optimization process is highly sensitive to the dataset quality. In this study, we propose a novel approach to augment Preference Optimization datasets. Additionally, we introduce a Multi-response-based Preference Optimization training method that enables the simultaneous learning of multiple responses.

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