Auditing Preference Biases and Fine-Tuning Language Models with Direct Preference Optimization on Anthropic HH-RLHF Using TRL and LoRA

This tutorial describes how to fine-tune language models using Direct Preference Optimization on a dataset of human feedback, specifically examining and correcting for biases in what responses are labeled as preferred versus rejected. The process involves loading conversation data, analyzing it for structural patterns that might unfairly favor certain responses, and then training a smaller language model while optionally using parameter-efficient adaptation techniques. The workflow includes evaluating how well the trained model learns to distinguish preferred responses and testing its performance across different data subsets to identify potential issues like length bias.