New Experimental AI-Based Research Tool For Literature Reviews: “Introducing Ai2 ScholarQA”

Ed. Note: For background on what’s shared below make sure to see our coverage of the launch of Open Scholar on November 19, 2024. Ai2 (Allen Institute for Artificial Intelligence) is the provider of Semantic Scholar.

From Ai2:

Literature review takes up a lot of time for researchers. While emerging AI tools can help get answers from a single paper, we found that researchers often need to compare and summarize multiple papers and understand the complex relationships between them. Ai2 ScholarQA is an experimental solution for this need — you can ask scientific questions that require multiple documents to answer. With table comparisons, expandable sections for subtopics, and citations with paper excerpts for verification, ScholarQA helps researchers get more in-depth, detailed, and contextual answers.

Ai2 ScholarQA follows a RAG-based, multi-step prompting workflow using a state-of-the-art closed model (Claude Sonnet 3.5). [Emphasis Ours] It relies on a corpus of open-access papers (e.g., arXiv).

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Ai2 ScholarQA is meant to satisfy literature searches that require insights from multiple relevant documents, and synthesize those insights into a comprehensive report

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Ai2 ScholarQA is an experimental solution to help researchers conduct literature reviews more efficiently by providing more in-depth answers. We built an evidence-first pipeline, where the model focuses on writing an answer built around evidence, rather than writing an answer and then trying to find evidence. We found that with this approach, the model may lose its ability to communicate coherently – for example, the model might be a little off-topic when trying to integrate the evidence the model is finding. The model wants to fit all the evidence into the answer, even if it’s only related in a limited or peripheral sense. Additionally, because the model is screening an enormous corpus to locate these evidence-based answers and is forming a more organized and in-depth answer, response times are longer than other models.

We will be open-sourcing the core functionality in the coming weeks. In the future, we will be exploring more ways to assist scientific research with AI, such as more personalization. By providing these resources and knowledge to the community, we hope to unlock more potential for AI to help accelerate science.

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See Also: Research Tools (Prototype): University of Washington & Allen Institute for AI (the People Behind Semantic Scholar) Announce the Launch of Ai2 Open Scholar (November 19, 2024)

Filed under: Journal Articles, News, Open Access