neoai-inc/LIT-RAGBench

LIT-RAGBench LIT-RAGBench is a benchmark for evaluating generator capabilities in Retrieval-Augmented Generation (RAG). It focuses on whether a model can answer questions correctly given retrieved documents, independent of retrieval quality. The benchmark covers five categories: Integration, Reasoning, Logic, Table, and Abstention. Dataset Summary LIT-RAGBench contains: 114 human-constructed Japanese questions An English version generated by machine translation… See the full description on the dataset page: https://huggingface.co/datasets/neoai-inc/LIT-RAGBench.