Auditing Preference Biases and Fine-Tuning Language Models with Direct Preference Optimization on Anthropic HH-RLHF Using TRL and LoRA
In this tutorial, we design an end-to-end preference-learning workflow utilizing the Anthropic HH-RLHF dataset and Direct Preference Optimization (DPO). We start by making ready a sturdy Colab surroundings, loading and parsing chosen–rejected response pairs, and auditing the dataset for structural and length-based choice biases. We then run lexical shortcut diagnostics to find out whether or…
