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cs.CL2024
FIRST: Teach A Reliable Large Language Model Through Efficient Trustworthy Distillation
KaShun Shum, Minrui Xu, Jianshu Zhang +5
Large language models (LLMs) have become increasingly prevalent in our daily lives, leading to an expectation for LLMs to be trustworthy -- - both accurate and well-calibrated (the…
cs.CL2024★ 6 cited
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
Cheng Niu, Yuanhao Wu, Juno Zhu +5
Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs). Despite the integration of RAG, LLMs may still pres…