CSEval: Towards Automated, Multi-Dimensional, and Reference-Free Counterspeech Evaluation using Auto-Calibrated LLMs
Amey Hengle, Aswini Kumar, Anil Bandhakavi, Tanmoy Chakraborty·January 29, 2025
Summary
CSEval introduces a dataset and framework for evaluating counterspeech quality across four dimensions: contextual-relevance, aggressiveness, argument-coherence, and suitableness. It proposes Auto-Calibrated COT for Counterspeech Evaluation (ACE), a prompt-based method with auto-calibrated chain-of-thoughts for scoring counterspeech using large language models. ACE outperforms traditional metrics like ROUGE, METEOR, and BertScore in correlating with human judgement, marking a significant advancement in automated counterspeech evaluation.
Introduction
Background
Overview of counterspeech and its importance
Challenges in evaluating counterspeech quality
Objective
Introduce CSEval: a dataset and framework for comprehensive counterspeech evaluation
Dataset (CSEval)
Structure and Components
Four dimensions for evaluating counterspeech quality
Detailed breakdown of each dimension
Data Collection
Methods for gathering diverse and representative counterspeech examples
Data Preprocessing
Techniques for preparing data for evaluation
Framework
Evaluation Criteria
Explanation of the four dimensions (contextual-relevance, aggressiveness, argument-coherence, suitableness)
Evaluation Method
Introduction of Auto-Calibrated COT for Counterspeech Evaluation (ACE)
Implementation
Description of the prompt-based method with auto-calibrated chain-of-thoughts
Methodology
Data Collection
Overview of the process for gathering and categorizing counterspeech examples
Data Preprocessing
Explanation of the steps taken to prepare the data for evaluation
Evaluation Process
Detailed description of the ACE method
Results
Comparison with Traditional Metrics
Performance of ACE against ROUGE, METEOR, and BertScore
Correlation with Human Judgement
Analysis of the correlation between ACE scores and human evaluations
Conclusion
Significance of CSEval
Importance of CSEval in advancing automated counterspeech evaluation
Future Directions
Potential improvements and future research based on CSEval
Advanced features