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2D-DPO: Scaling Direct Preference Optimization with 2-Dimensional Supervision (2410.19720v1)

Published 25 Oct 2024 in cs.CL and cs.AI

Abstract: Recent advancements in Direct Preference Optimization (DPO) have significantly enhanced the alignment of LLMs with human preferences, owing to its simplicity and effectiveness. However, existing methods typically optimize a scalar score or ranking reward, thereby overlooking the multi-dimensional nature of human preferences. In this work, we propose to extend the preference of DPO to two dimensions: segments and aspects. We first introduce a 2D supervision dataset called HelpSteer-2D. For the segment dimension, we divide the response into sentences and assign scores to each segment. For the aspect dimension, we meticulously design several criteria covering the response quality rubrics. With the 2-dimensional signals as feedback, we develop a 2D-DPO framework, decomposing the overall objective into multi-segment and multi-aspect objectives. Extensive experiments on popular benchmarks demonstrate that 2D-DPO performs better than methods that optimize for scalar or 1-dimensional preferences.

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Citations (1)
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Summary

  • The paper introduces a two-dimensional supervision framework that integrates segment and aspect-level feedback to enhance LLM alignment.
  • The methodology decomposes responses into segments scored across attributes like helpfulness, correctness, and clarity for refined optimization.
  • Experimental results on benchmarks show that 2D-DPO outperforms standard DPO in aligning models with nuanced human preferences.

2D-DPO: Scaling Direct Preference Optimization with 2-Dimensional Supervision

The paper "2D-DPO: Scaling Direct Preference Optimization with 2-Dimensional Supervision" addresses the limitations of traditional Direct Preference Optimization (DPO) methods in aligning LLMs with human preferences. The authors propose a novel approach, 2D-DPO, which enhances the alignment process by integrating two-dimensional supervision characterized by both segment and aspect-level feedback.

Background and Motivation

Direct Preference Optimization has emerged as a promising alternative to traditional Reinforcement Learning from Human Feedback (RLHF), primarily due to its simplicity and effectiveness in bypassing the need for an explicit reward model. However, conventional DPO approaches are limited to scalar or ranking-based rewards, which fail to capture the multidimensional nature of human preferences. This shortcoming can lead to suboptimal alignment decisions, as different segments of a response may vary significantly in quality across various criteria such as correctness, clarity, and completeness.

2D-DPO Methodology

The proposed 2D-DPO framework introduces a more nuanced alignment strategy through two critical innovations:

  1. Construction of a 2D Supervision Dataset (HelpSteer-2D):
    • Responses are segmented into sentences, each scored across multiple aspects including helpfulness, correctness, safety, completeness, and clarity.
    • This multi-aspect, multi-segment approach allows for a finely-tuned feedback mechanism that better reflects the intricate nature of human evaluation.
  2. 2D-DPO Framework:
    • Utilizes the scores from HelpSteer-2D to decompose the overall objective into multi-segment and multi-aspect optimization tasks.
    • Adjusts the token-level advantage function, allowing the model to recognize varying importance across different response segments and aspects.
    • The innovative framework effectively scales the supervision signals, enhancing model alignment through targeted feedback for different dimensions.

Experimental Validation

The paper presents extensive experiments on popular benchmarks (Arena-Hard, AlpacaEval 2.0, and MT-Bench), demonstrating the superior performance of 2D-DPO over existing methods such as standard DPO and token-level preference optimization techniques. The results indicate significant improvements in aligning with human preferences without introducing additional verbosity or compromising the fundamental abilities of the LLMs.

Implications and Future Directions

The 2D-DPO method addresses a critical gap in preference optimization by recognizing and accommodating the dynamic nature of human feedback. This advancement can lead to more robust LLMs that are better aligned with user intentions across diverse scenarios.

Looking forward, the approach offers promising avenues for online training and iterative alignment. The development of reward models capable of generating 2D feedback signals could facilitate ongoing adaptation of LLMs to evolving human preferences more cost-effectively.

Moreover, this work invites future exploration of multi-dimensional preference frameworks, potentially expanding beyond two dimensions to capture an even broader spectrum of human evaluative criteria. This could include context-dependent aspects or dynamic weighting of criteria based on user-specific needs, significantly enhancing the personalization and efficacy of human-computer interactions.

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