Negotiating LLMs for Enhanced Hate Speech Classification and Interpretability - Assaoka, João; Berton, Lilian (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), 2025)
Traditional hate speech classification frameworks rely on a single Large Language Model (LLM) operating in an isolated, single-turn decision-making process. However, this approach can suffer from limitations in handling nuanced linguistic phenomena such as sarcasm, ambiguity, and contextual shifts. To address these challenges, we introduce a multi-LLM negotiation framework, where two specialized models engage in iterative exchanges to refine the classification decision. The generator proposes a classification alongside a rationale, while the discriminator evaluates its credibility and requests adjustments until a consensus is reached. Experiments conducted on Twitter in Portuguese hate speech detection dataset, demonstrate that the negotiation-based approach using Sabizinho 3 and GPT 4.1 Nano with zero-shot, achieve competitive precision and recall. Furthermore, results indicate that this framework allow interpretability of the final classification.