HEAL: A Hypothesis-Based Preference-Aware Analysis Framework

ACL ARR 2025 May Submission7016 Authors

Abstract:

Preference optimization methods like DPO have achieved remarkable performance in LLM alignment.
However, the evaluation for these methods relies on a single response and overlooks other potential outputs, which could also be generated in real-world applications within this hypothetical space.
To address this issue, this paper presents a Hypothesis-based PrEference-aware AnaLysis Framework (HEAL), a novel evaluation paradigm that formulates preference alignment as a re-ranking process within hypothesis spaces.
The framework incorporates two complementary metrics: ranking accuracy for evaluating ordinal consistency and preference strength correlation for assessing continuous alignment.
To facilitate this framework, we develop UniHypoBench, a unified hypothesis benchmark constructed from diverse instruction-response pairs. Through extensive experiments based on HEAL, with a particular focus on the intrinsic mechanisms of preference learning, we demonstrate that current preference learning methods can effectively capture preferences provided by proxy models while simultaneously suppressing negative samples.
These findings contribute to preference learning research through two significant avenues. Theoretically, we introduce hypothesis space analysis as an innovative paradigm for understanding preference alignment. Practically, HEAL offers researchers robust diagnostic tools for refining preference optimization methods, while our empirical results identify promising directions for developing more advanced alignment algorithms capable of comprehensive preference capture.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: DPO,preference optimization method,interpretability and analysis,large language model

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 7016