Direct Preference Optimization for Chatbot Fine-Tuning: An Empirical Study
This work offers a simpler and more efficient alternative for fine-tuning chatbots, but the improvements are incremental and the instability problem limits its immediate impact.
The paper investigates Direct Preference Optimization (DPO) for fine-tuning large language models, showing it simplifies training, improves efficiency, and achieves competitive performance on metrics like BLEU, ROUGE, and cosine similarity, though training instability remains an issue.
We present an approach to fine-tuning large language models using Direct Preference Optimization (DPO), a reinforcement learning technique. Our experimental results demonstrate that DPO simplifies the training pipeline, improves computational efficiency, and achieves competitive performance. The evaluation using BLEU, ROUGE, and cosine similarity metrics indicates effective learning and convergence, though further investigation is needed to address observed training instability.