Direct Preference Optimization versus Supervised Fine-Tuning for Counter-Speech Coherence and Argumentative Strength in

Abstract

Abstract: The automatic generation of counter-speech (CS) is a critical strategy for addressing hate speech by providing constructive and informed responses. However, existing methods often fail to generate high-quality, impactful, and scalable CS, particularly across diverse linguistic contexts. In this paper, we propose a novel methodology to enhance CS generation by aligning Large Language Models (LLMs) using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Our approach leverages DPO to align LLM outputs with human preferences, ensuring contextually appropriate and linguisticall

Research Question

What is the impact of Direct Preference Optimization versus Supervised Fine-Tuning on the semantic coherence and argumentative strength of counter-speech generated by LLMs across low-resource languages?

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