ibm-research/REAL-MM-RAG_FinTabTrainSet

REAL-MM-RAG_FinTabTrainSet We curated a table-focused finance dataset from FinTabNet (Zheng et al., 2021), extracting richly formatted tables from S&P 500 filings. We used an automated pipeline in which queries were generated by a vision-language model (VLM) and filtered by a large language model (LLM). We generated 48,000 natural-language (query, answer, page) triplets to improve retrieval models on table-intensive financial documents. For more information, see the project… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/REAL-MM-RAG_FinTabTrainSet.

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