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MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents
Conference on Empirical Methods in Natural Language ProcessingRecognizing if LLM output can be grounded in evidence is central to many tasks in NLP: retrieval-augmented generation, summarization, document-grounded dialogue, and more. Current approaches to this kind of fact-checking are based on verifying each piece
Liyan Tang, Philippe Laban, Greg Durrett
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Knowledge Graph-Guided Retrieval Augmented Generation
North American Chapter of the Association for Computational LinguisticsRetrieval-augmented generation (RAG) has emerged as a promising technology for addressing hallucination issues in the responses generated by large language models (LLMs).
Xiangrong Zhu +4 more
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Rethinking Chunk Size For Long-Document Retrieval: A Multi-Dataset Analysis
arXiv.orgChunking is a crucial preprocessing step in retrieval-augmented generation (RAG) systems, significantly impacting retrieval effectiveness across diverse datasets.
Sinchana Ramakanth Bhat +3 more
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Papers presented at the May 3-5, 1960, western joint IRE-AIEE-ACM computer conference on - IRE-AIEE-ACM '60 (Western), 1960
The Fact Compiler is a system for the timely extraction of significant information from source data and for the storage of this information in an organized manner that permits rapid retrieval. In addition, the Fact Compiler can process or manipulate the stored data in a variety of ways, and it is adaptable for use with presend-day reporting techniques.
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The Fact Compiler is a system for the timely extraction of significant information from source data and for the storage of this information in an organized manner that permits rapid retrieval. In addition, the Fact Compiler can process or manipulate the stored data in a variety of ways, and it is adaptable for use with presend-day reporting techniques.
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arXiv.org
Retrieval-augmented generation (RAG) has shown impressive capabilities in mitigating hallucinations in large language models (LLMs). However, LLMs struggle to maintain consistent reasoning when exposed to misleading or conflicting evidence, especially in
Linda Zeng +4 more
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Retrieval-augmented generation (RAG) has shown impressive capabilities in mitigating hallucinations in large language models (LLMs). However, LLMs struggle to maintain consistent reasoning when exposed to misleading or conflicting evidence, especially in
Linda Zeng +4 more
semanticscholar +1 more source
Children’s Retrieval of Science Facts: The Role of Hints and Confidence
MemoryThe effortful process of retrieving information from memory has been established as an effective strategy for improving student learning. However, we have a limited understanding of the development of retrieval practice in children, including contexts that may scaffold its benefit.
Elisabeth C, McLane, Diana, Selmeczy
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The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models
Annual International ACM SIGIR Conference on Research and Development in Information RetrievalLarge Language Models (LLMs) have significantly enhanced the capabilities of information access systems, especially with retrieval-augmented generation (RAG).
Ronak Pradeep +5 more
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A Language And Data Structure For Fact Retrieval.
1971PhD ; Computer science ; University of Michigan, Horace H. Rackham School of Graduate Studies ; http://deepblue.lib.umich.edu/bitstream/2027.42/186928/2/7214792 ...
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