Explanation Benchmark

Explanation benchmarks are datasets and evaluation frameworks designed to assess the explainability of complex machine learning models, particularly in natural language processing. Current research focuses on developing comprehensive benchmarks across diverse domains (e.g., medical, industrial) and task types, often incorporating structured explanations and multi-step reasoning. These benchmarks aim to improve the transparency and trustworthiness of AI systems by providing standardized methods for evaluating and comparing different explanation techniques, ultimately leading to more reliable and understandable AI models for various applications.