A Multi-Aspect Framework for Counter Narrative Evaluation using Large Language Models

Jaylen Jones, Lingbo Mo, Eric Fosler-Lussier, Huan Sun

Abstract
Counter narratives - informed responses to hate speech contexts designed to refute hateful claims and de-escalate encounters - have emerged as an effective hate speech intervention strategy. While previous work has proposed automatic counter narrative generation methods to aid manual interventions, the evaluation of these approaches remains underdeveloped. Previous automatic metrics for counter narrative evaluation lack alignment with human judgment as they rely on superficial reference comparisons instead of incorporating key aspects of counter narrative quality as evaluation criteria. To address prior evaluation limitations, we propose a novel evaluation framework prompting LLMs to provide scores and feedback for generated counter narrative candidates using 5 defined aspects derived from guidelines from counter narrative specialized NGOs. We found that LLM evaluators achieve strong alignment to human-annotated scores and feedback and outperform alternative metrics, indicating their potential as multi-aspect, reference-free and interpretable evaluators for counter narrative evaluation.
Anthology ID:
2024.naacl-short.14
Volume:
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
Month:
June
Year:
2024
Address:
Mexico City, Mexico
Editors:
Kevin Duh, Helena Gomez, Steven Bethard
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
147–168
Language:
URL:
https://aclanthology.org/2024.naacl-short.14
DOI:
Bibkey:
Cite (ACL):
Jaylen Jones, Lingbo Mo, Eric Fosler-Lussier, and Huan Sun. 2024. A Multi-Aspect Framework for Counter Narrative Evaluation using Large Language Models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 147–168, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
A Multi-Aspect Framework for Counter Narrative Evaluation using Large Language Models (Jones et al., NAACL 2024)
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