Online hate detection suffers from biases incurred in data sampling, annotation, and model pre-training. Therefore, measuring the averaged performance over all examples in held-out test data is inadequate. Instead, we must identify specific model weaknesses and be informed when it is more likely to fail. A recent proposal in this direction is HateCheck, a suite for testing fine-grained model functionalities on synthesized data generated using templates of the kind "You are just a [slur] to me." However, despite enabling more detailed diagnostic insights, the HateCheck test cases are often generic and have simplistic sentence structures that do not match the real-world data. To address this limitation, we propose GPT-HateCheck, a framework to generate more diverse and realistic functional tests from scratch by instructing large language models (LLMs). We employ an additional natural language inference (NLI) model to verify the generations. Crowd-sourced annotation demonstrates that the generated test cases are of high quality. Using the new functional tests, we can uncover model weaknesses that would be overlooked using the original HateCheck dataset.

在线仇恨检测中存在由数据采样、标注和模型预训练导致的偏见。本研究提出了GPT-HateCheck框架，通过指导大型语言模型自动生成更多样化和真实的功能测试用例来解决现有数据集的限制，并以众包注释验证生成的测试用例质量，从而发现使用原始HateCheck数据集时可能忽视的模型弱点。

GPT-HateCheck：LLM能否为仇恨言论检测编写更好的功能性测试？