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Yushi Sun

2 accepted papers

2025

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering

EMNLP 2025

Retrieval-Augmented Generation (RAG) mitigates hallucination in Large Language Models (LLMs) by incorporating external data, with Knowledge Graphs (KGs) offering crucial information for question answering. Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing, which

2024

CRAG - Comprehensive RAG Benchmark

NeurIPS 2024poster

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, however, do not adequately represent the diverse and dynamic nature of real-world Question Answering (QA) tasks. To brid…