免费开源的学术问答系统，支持精准文献溯源。
Ai2 Scholar QA: Organized Literature Synthesis with Attribution
- 基于检索增强生成，可定制化部署
- 在最新科学问答基准上超越现有系统
- 适合科研人员快速获取文献依据
检索增强生成在回答科学文献问题方面日益有效，但许多先进系统成本高且闭源。我们推出 Ai2 Scholar QA，一款免费的在线科学问答应用。为促进研究，我们公开了完整流程：可定制的开源 Python 包、交互式网页应用，以及可通过公共 API 访问的论文索引和可下载数据集。我们详细描述了系统设计，并通过实验分析关键决策。在最近的科学问答基准测试中，Ai2 Scholar QA 表现优于现有系统。
原文摘要 · Abstract (English)
Retrieval-augmented generation is increasingly effective in answering scientific questions from literature, but many state-of-the-art systems are expensive and closed-source. We introduce Ai2 Scholar QA, a free online scientific question answering application. To facilitate research, we make our entire pipeline public: as a customizable open-source Python package and interactive web app, along with paper indexes accessible through public APIs and downloadable datasets. We describe our system in detail and present experiments analyzing its key design decisions. In an evaluation on a recent scientific QA benchmark, we find that Ai2 Scholar QA outperforms competing systems.
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