A RAG pipeline with OCR, table and image to text conversion, and a RAFT-tuned reranker reports high QA scores, but its 50-question evaluation overlaps with its training manuals and its baseline comparison uses only 5 questions.
: Factuality challenges in the era of large language models and opportunities for fact-checking
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LLM-Assisted Question-Answering on Technical Documents Using Structured Data-Aware Retrieval Augmented Generation
A RAG pipeline with OCR, table and image to text conversion, and a RAFT-tuned reranker reports high QA scores, but its 50-question evaluation overlaps with its training manuals and its baseline comparison uses only 5 questions.