A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation
This addresses the challenge of poor correlation between traditional metrics and human judgments in counter-narrative generation, offering a more reliable evaluation method for researchers and practitioners in NLP and content moderation.
The paper tackles the problem of evaluating counter-narrative generation by proposing a novel LLM-based ranking method, achieving a high correlation with human preference (ρ=0.88). It also analyzes LLMs as zero-shot generators, identifying chat-aligned models as the best option when they do not refuse due to security concerns.
This paper proposes a novel approach to evaluate Counter Narrative (CN) generation using a Large Language Model (LLM) as an evaluator. We show that traditional automatic metrics correlate poorly with human judgements and fail to capture the nuanced relationship between generated CNs and human perception. To alleviate this, we introduce a model ranking pipeline based on pairwise comparisons of generated CNs from different models, organized in a tournament-style format. The proposed evaluation method achieves a high correlation with human preference, with a $ρ$ score of 0.88. As an additional contribution, we leverage LLMs as zero-shot CN generators and provide a comparative analysis of chat, instruct, and base models, exploring their respective strengths and limitations. Through meticulous evaluation, including fine-tuning experiments, we elucidate the differences in performance and responsiveness to domain-specific data. We conclude that chat-aligned models in zero-shot are the best option for carrying out the task, provided they do not refuse to generate an answer due to security concerns.