Ai2 Scholar QA: Organized Literature Synthesis with Attribution
This provides a more accessible tool for researchers and practitioners in scientific literature synthesis, though it is incremental as it builds on existing retrieval-augmented generation methods.
The authors tackled the problem of expensive and closed-source retrieval-augmented generation systems for scientific question answering by introducing Ai2 Scholar QA, a free and open-source application that outperforms competing systems on a recent benchmark.
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.