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.
114 human-constructed Japanese questions
An English version generated by machine translation with… See the full description on the dataset page:
https://huggingface.co/datasets/neoai-inc/LIT-RAGBench.
