Title: Robust Preference Alignment via Directional Neighborhood Consensus
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Large Language Models, Preference Alignment, Inference-Time Method

Abstract: Aligning large language models with human preferences is critical for creating
reliable and controllable AI systems. A human preference can be visualized as a
high-dimentional vector where different directions represent trade-offs between
desired attributes (e.g., helpfulness vs. verbosity). Yet, because the training
data often reflects dominant, average preferences, LLMs tend to perform well on
common requests but falls short in specific, individual needs. This mismatch creates
a preference coverage gap. Existing methods often address this through costly
retraining, which may not be generalized to the full spectrum of diverse preferences.
This brittleness means that when a user’s request reflects a nuanced preference
deviating from the training data’s central tendency, model performance can degrade
unpredictably. To address this challenge, we introduce Robust Preference Selection
(RPS), a post-hoc, training-free method by leveraging directional neighborhood
consensus. Instead of forcing a model to generate a response from a single, highly
specific preference, RPS samples multiple responses from a local neighborhood of
related preferences to create a superior candidate pool. It then selects the response
that best aligns with the user’s original intent. We provide a theoretical framework
showing our neighborhood generation strategy is provably superior to a strong
baseline that also samples multiple candidates. Comprehensive experiments across
three distinct alignment paradigms (DPA, DPO, and SFT) demonstrate that RPS
consistently improves robustness against this baseline, achieving win rates of up
to 69% on challenging preferences from under-represented regions of the space
without any model retraining. Our work presents a practical, theoretically-grounded
solution for enhancing the reliability of preference-aligned models1.