Improving LLM Safety and Helpfulness using SFT and DPO: A Study on OPT-350M
This study addresses the problem of aligning language models for better safety and helpfulness, which is crucial for AI developers and users, but it is incremental as it builds on existing techniques.
This research tackled improving the safety and helpfulness of the OPT-350M language model using alignment techniques like SFT and DPO, finding that a combined SFT+DPO approach outperformed others across all metrics, including Harmlessness Rate, Helpfulness Rate, and Combined Alignment Score.
This research investigates the effectiveness of alignment techniques, Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and a combined SFT+DPO approach on improving the safety and helpfulness of the OPT-350M language model. Utilizing the Anthropic Helpful-Harmless RLHF dataset, we train and evaluate four models: the base OPT350M, an SFT model, a DPO model, and a model trained with both SFT and DPO. We introduce three key evaluation metrics: Harmlessness Rate (HmR), Helpfulness Rate (HpR), and a Combined Alignment Score (CAS), all derived from reward model outputs. The results show that while SFT outperforms DPO, The combined SFT+DPO model outperforms all others across all metrics, demonstrating the complementary nature of these techniques. Our findings also highlight challenges posed by noisy data, limited GPU resources, and training constraints. This study offers a comprehensive view of how fine-tuning strategies affect model alignment and provides a foundation for more robust alignment pipelines in future work.