Effect of SFT+DPO Alignment on Helpfulness Retention in OPT-350M

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

Research goal: What is the effect of the SFT+DPO alignment strategy on the helpfulness retention rate of OPT-350M when evaluated on the Anthropic Helpful-Harmless dataset?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.