Toward Systematic Counterfactual Fairness Evaluation of Large Language Models: The CAFFE Framework

This article introduces the CAFFE framework for evaluating the counterfactual fairness of Large Language Models (LLMs). The focus is on systematic evaluation, suggesting a structured approach to assessing fairness, which is a crucial aspect of responsible AI development. The use of 'counterfactual' implies the framework explores how model outputs change under different hypothetical scenarios, allowing for a deeper understanding of potential biases. The source being ArXiv indicates this is a research paper, likely detailing the framework's methodology, implementation, and experimental results.

• •Focus on counterfactual fairness evaluation.
• •Introduces the CAFFE framework.
• •Aims for a systematic approach to fairness assessment.
• •Relevant to responsible AI development and LLMs.