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

A new technical tutorial outlines a workflow for fine-tuning language models using Direct Preference Optimization. The process includes methods for auditing the Anthropic HH-RLHF dataset for structural and length-based biases before implementing training pipelines with TRL and LoRA.