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Juno Zhu

3 accepted papers

2024

RAG-HAT: A Hallucination-Aware Tuning Pipeline for LLM in Retrieval-Augmented Generation

EMNLP 2024industry

Retrieval-augmented generation (RAG) has emerged as a significant advancement in the field of large language models (LLMs). By integrating up-to-date information not available during their initial training, RAG greatly enhances the practical utility of LLMs in real-world applications. However, even…

Cited by 5SourcePDFScholar
2024

RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models

ACL 2024long

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 present unsupported or contradictory claims to the retrieved contents. In order to develop effective hallucination preventio…

2024

VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning

ACL 2024system demonstrations

The proliferation of fake news poses a significant threat not only by disseminating misleading information but also by undermining the very foundations of democracy. The recent advance of generative artificial intelligence has further exacerbated the challenge of distinguishing genuine news from fab…

Cited by 2SourcePDFScholar