DPO and SFT Comparison in LLM Counter-Speech Argumentation Across Languages

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 DPO versus SFT on the argumentative strength metrics of LLM-generated counter-speech across diverse linguistic contexts in alignment evaluations?

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