Behavioral Testing

Behavioral testing in NLP aims to evaluate model capabilities beyond traditional accuracy metrics by assessing their responses to specifically designed inputs, revealing underlying biases and weaknesses. Current research focuses on automating test case generation using large language models (LLMs) and applying these methods to various NLP tasks, including machine translation, sentiment analysis, and even clinical applications like depression detection and therapeutic chatbots. This approach enhances model interpretability, identifies problematic behaviors, and ultimately contributes to the development of more robust and reliable NLP systems with improved generalization and reduced biases.