CLAPNQ：面向RAG系统的基于Natural Questions中段落的连贯长文本答案
CLAPNQ: Cohesive Long-form Answers from Passages in Natural Questions for RAG systems
AI总结：
提出了ClapNQ，一个用于完整RAG流水线的基准长文本问答数据集，包含来自NQ的简洁连贯长答案及语料库，并通过基线实验揭示了基础RAG系统仍有显著改进空间。
摘要：
检索增强生成（RAG）已成为大型语言模型的一种流行应用。成功的RAG系统最好能提供准确的答案，这些答案以段落为基础得到支持，且没有任何幻觉。虽然构建完整的RAG流水线需要大量工作，但能够对性能进行基准测试也是必要的。我们提出了ClapNQ，一个用于完整RAG流水线的基准长文本问答数据集。ClapNQ包含来自Natural Questions (NQ) 的带有基础黄金段落的长答案，以及一个语料库，用于执行检索、生成或完整的RAG流水线。ClapNQ的答案简洁，比完整段落小3倍，并且具有连贯性，这意味着答案流畅地组合而成，通常通过整合段落中不连续的多个片段。RAG模型必须适应这些特性才能在ClapNQ上取得成功。我们展示了ClapNQ的基线实验和分析，突出了基础RAG仍有显著改进空间的地方。CLAPNQ已在https://github.com/primeqa/clapnq上公开发布。
英文摘要：
Retrieval Augmented Generation (RAG) has become a popular application for large language models. It is preferable that successful RAG systems provide accurate answers that are supported by being grounded in a passage without any hallucinations. While considerable work is required for building a full RAG pipeline, being able to benchmark performance is also necessary. We present ClapNQ, a benchmark Long-form Question Answering dataset for the full RAG pipeline. ClapNQ includes long answers with grounded gold passages from Natural Questions (NQ) and a corpus to perform either retrieval, generation, or the full RAG pipeline. The ClapNQ answers are concise, 3x smaller than the full passage, and cohesive, meaning that the answer is composed fluently, often by integrating multiple pieces of the passage that are not contiguous. RAG models must adapt to these properties to be successful at ClapNQ. We present baseline experiments and analysis for ClapNQ that highlight areas where there is still significant room for improvement in grounded RAG. CLAPNQ is publicly available at https://github.com/primeqa/clapnq
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
