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Xiangkun Hu
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Author, Amazon Science
China
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RefChecker: Reference-based fine-grained hallucination checker and benchmark for large language models
By Xiangkun Hu, Dongyu Ru, Tianhang Zhang, Zheng Zhang| Amazon Science@ Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents REFCHECKER, a framework that introduces claim-triplets to represent claims in LLM responses, aiming to detect fine-grained hallucinations. In REFCHECKER, an extractor generates claim-triplets from a response, which are then evaluated by a checker against a reference.
By Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo| Amazon Science@ Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents REFCHECKER, a framework that introduces claim-triplets to represent claims in LLM responses, aiming to detect fine-grained hallucinations. In REFCHECKER, an extractor generates claim-triplets from a response, which are then evaluated by a checker against a reference.
RefChecker: Reference-based fine-grained hallucination checker and benchmark for large language models
By Xiangkun Hu, Dongyu Ru, Tianhang Zhang, Zheng Zhang| Amazon Science@ Large Language Models (LLMs) have shown impressive capabilities but also a concerning tendency to hallucinate. This paper presents REFCHECKER, a framework that introduces claim-triplets to represent claims in LLM responses, aiming to detect fine-grained hallucinations. In REFCHECKER, an extractor generates claim-triplets from a response, which are then evaluated by a checker against a reference.
Company Info
Amazon Science