# HEAL: A Hypothesis-Based Preference-Aware Analysis Framework

## TL;DR

HEAL introduces a hypothesis-based evaluation framework for preference alignment in LLMs, addressing limitations of existing single-response assessments and providing new diagnostic tools and insights for preference learning.

## Contribution

This paper presents HEAL, a novel hypothesis-based evaluation framework and UniHypoBench benchmark, enhancing the assessment and understanding of preference alignment in language models.

## Key findings

- Preference learning methods effectively capture proxy model preferences.
- Current methods can suppress negative samples.
- HEAL provides robust diagnostic tools for preference optimization.

## 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 \textbf{H}ypothesis-based Pr\textbf{E}ference-aware \textbf{A}na\textbf{L}ysis 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.