8 citations · 9 across the 10 of their papers we have counts for
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cs.CL2024
Review-Then-Refine: A Dynamic Framework for Multi-Hop Question Answering with Temporal Adaptability
Xiangsen Chen, Xuming Hu, Nan Tang
Retrieve-augmented generation (RAG) frameworks have emerged as a promising solution to multi-hop question answering(QA) tasks since it enables large language models (LLMs) to incor…
cs.CL2024
Are Large Language Models a Good Replacement of Taxonomies?
Yushi Sun, Hao Xin, Kai Sun +5
Large language models (LLMs) demonstrate an impressive ability to internalize knowledge and answer natural language questions. Although previous studies validate that LLMs perform…
cs.CL2024★ 8 cited
CRAG -- Comprehensive RAG Benchmark
Xiao Yang, Kai Sun, Hao Xin +24
Retrieval-Augmented Generation (RAG) has recently emerged as a promising solution to alleviate Large Language Model (LLM)'s deficiency in lack of knowledge. Existing RAG datasets,…