Conformal Feedback Alignment: Quantifying Answer-Level Reliability for Robust LLM Alignment

2026-01-24

AI Summary

This work proposes Conformal Feedback Alignment (CFA), a novel framework that integrates answer-level reliability modeling into large language model alignment—addressing a key limitation of existing preference alignment methods like RLHF, which rely on noisy human annotations and only account for preference-level uncertainty while ignoring the intrinsic reliability of individual responses. CFA leverages conformal prediction to construct statistically guaranteed confidence sets for each candidate answer and translates these into dynamic preference weights within both DPO and PPO optimization paradigms. Experimental results across multiple benchmarks demonstrate that CFA significantly enhances alignment robustness and data efficiency, thereby validating the importance and efficacy of explicitly modeling uncertainty at the answer level.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Natural Language Processing: Question Answering
• Humans and AI: Learning Human Values and Preferences

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Preference-based alignment like Reinforcement Learning from Human Feedback (RLHF) learns from pairwise preferences, yet the labels are often noisy and inconsistent. Existing uncertainty-aware approaches weight preferences, but ignore a more fundamental factor: the reliability of the \emph{answers} being compared. To address the problem, we propose Conformal Feedback Alignment (CFA), a framework that grounds preference weighting in the statistical guarantees of Conformal Prediction (CP). CFA quantifies answer-level reliability by constructing conformal prediction sets with controllable coverage and aggregates these reliabilities into principled weights for both DPO- and PPO-style training. Experiments across different datasets show that CFA improves alignment robustness and data efficiency, highlighting that modeling \emph{answer-side} uncertainty complements preference-level weighting and yields more robust, data-efficient alignment. Codes are provided here.

Problem

• LLM alignment
• preference-based learning
• answer-level reliability
• noisy labels
• uncertainty quantification

Innovation

• Conformal Prediction
• Answer-Level Reliability
• Preference-Based Alignment
• Robust LLM Alignment
• Uncertainty Quantification