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Título del libro:
Título del capítulo: NLP@IIMAS-CLTL at Multilingual Counterspeech Generation: Combating Hate Speech Using Contextualized Knowledge Graph Representations and LLMs

Autores UNAM:
HELENA MONTSERRAT GOMEZ ADORNO;

Autores externos:

Idioma:

Año de publicación:
2025

Palabras clave:

Computational linguistics; Contextualized knowledge; Evaluation metrics; Generative model; Graph representation; Graph-based; Knowledge graphs; Language model; Model based approach; Knowledge graph

Resumen:

We present our approach for the shared task on Multilingual Counterspeech Generation (MCG) to counteract hate speech (HS) in Spanish, English, Basque, and Italian. To accomplish this, we followed two different strategies: 1) a graph-based generative model that encodes graph representations of knowledge related to hate speech, and 2) leveraging prompts for a large language model (LLM), specifically GPT-4o. We find that our graph-based approach tends to perform better in terms of traditional evaluation metrics (i.e., RougeL, BLEU, BERTScore), while the JudgeLM evaluation employed in the shared task favors the counter-narratives generated by the LLM-based approach, which was ranked second for English and third for Spanish on the leaderboard. © 2025 Association for Computational Linguistics

Entidades citadas de la UNAM:

Título del libro: Título del capítulo: NLP@IIMAS-CLTL at Multilingual Counterspeech Generation: Combating Hate Speech Using Contextualized Knowledge Graph Representations and LLMs

Título del libro:
Título del capítulo: NLP@IIMAS-CLTL at Multilingual Counterspeech Generation: Combating Hate Speech Using Contextualized Knowledge Graph Representations and LLMs