SEMQA: Semi-Extractive Multi-Source Question Answering

Topics: AI (Deep Learning), Retrieval Augmented Generation (RAG), SGE

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The paper “SEMQA: Semi-Extractive Multi-Source Question Answering” introduces a new QA task where models generate comprehensive answers by combining verbatim quotes from multiple sources with free-text connectors, enhancing verifiability and evaluation ease. The study presents QuoteSum, a dataset of human-written semi-extractive answers, and demonstrates the effectiveness and challenges of this approach through various experiments and evaluations with large language models.

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