7 citations · 14 across the 2 of their papers we have counts for
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cs.CL2024★ 7 cited
Knowledge Overshadowing Causes Amalgamated Hallucination in Large Language Models
Yuji Zhang, Sha Li, Jiateng Liu +5
Hallucination is often regarded as a major impediment for using large language models (LLMs), especially for knowledge-intensive tasks. Even when the training corpus consists solel…
cs.CL2024★ 7 cited
Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debate
Kyungha Kim, Sangyun Lee, Kung-Hsiang Huang +3
Fact-checking research has extensively explored verification but less so the generation of natural-language explanations, crucial for user trust. While Large Language Models (LLMs)…