Impact of Rationale-Guided Preference Alignment on LLM Calibration and Confidence Under Syntactic Distractors in HANS Versus RLHF

Abstract

Abstract: Fine-grained control over large language models (LLMs) remains a significant challenge, hindering their adaptability to diverse user needs. While Reinforcement Learning from Human Feedback (RLHF) shows promise in aligning LLMs, its reliance on scalar rewards often limits its ability to capture diverse user preferences in real-world applications. To address this limitation, we introduce the Directional Preference Alignment (DPA) framework. Unlike the scalar-reward RLHF, DPA incorporates multi-objective reward modeling to represent diverse preference profiles. Additionally, DPA models user prefe

Research Question

What is the impact of rationale-guided preference alignment on the calibration and confidence scores of LLMs when facing syntactic distractors in the HANS benchmark versus traditional RLHF methods?

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