A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation

TL;DR

This paper introduces an LLM-based evaluation method for Counter-Narrative generation that correlates well with human judgment and compares different LLM models' capabilities in generating CNs.

Contribution

It presents a novel ranking evaluation pipeline using pairwise comparisons with LLMs and analyzes various LLMs as zero-shot CN generators, highlighting their strengths and limitations.

Findings

High correlation (ρ=0.88) with human preferences.

Chat-aligned models excel in zero-shot CN generation.

Fine-tuning impacts model performance and responsiveness.

Abstract

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 $\rho$ 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.