Direct Preference Optimization for Robust Counter-Speech Models in Low-Resource Languages

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: To what extent does Direct Preference Optimization enhance the robustness of counter-speech models against adversarial hate speech inputs compared to Supervised Fine-Tuning in low-resource language settings?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.