Steerable Cultural Preference Optimization of Reward Models
It addresses the problem of cultural bias in LLM alignment for developers serving diverse global subcommunities, offering a method to mitigate bias and improve preference representation.
This paper introduces SCPO, a novel reward model training algorithm that incorporates diverse cultural preferences in a balanced manner, achieving up to 7 points improvement in minority reward model performance across two datasets and 7 countries, and up to 280% more training data-efficient than full-data finetuning.
It is essential for large language model (LLM) technology to serve many different cultural sub-communities in a manner that is acceptable to each community. However, research on LLM alignment has so far predominantly focused on predicting a unified response preference of annotators from certain regions. This paper aims to advance the development of alignment models with a more global outlook, that are able to accurately represent the preferences of subcommunities and do not exhibit excessive bias towards any of them. We focus on the development of reward models for this purpose and present a novel reward model training algorithm (SCPO) that can incorporate diverse cultural preferences in a balanced manner. Our method results in performance increases of the minority reward model of up to 7 points over the baseline model across two datasets, PRISM and GlobalOpinionQA, and across 7 countries. SCPO is up to 280% more training data-efficient than full-data finetuning of reward models. In addition, we perform analysis of bias by separately evaluating on the preference of subcommunities and show that excessive bias is mitigated via our weighting method. Our code is available at https://github.com/minsik-ai/Steerable-Cultural-Preference