Trade-off Between Harmlessness Rate and Helpfulness in DPO-aligned OPT-350M Models Across XTREME-R Language Subsets

Abstract

Abstract: 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 Question

Does the trade-off between Harmlessness Rate and Helpfulness in DPO-aligned OPT-350M models vary significantly across low-resource language subsets in the XTREME-R benchmark compared to high-resource languages?

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