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Elicit leverages language models to automate research workflows, such as conducting literature reviews. It identifies relevant papers without requiring exact keyword matches, summarizes findings specific to your research questions, and extracts key data from documents. Additionally, Elicit supports various research tasks including brainstorming, summarization, and text classification.
To use Elicit, enter your research questions in natural language. The AI will search, summarize, extract data, and interact with over 125 million academic papers to provide answers and insights.
Researchers typically use Elicit to accelerate literature reviews, discover papers that are difficult to find elsewhere, automate systematic reviews and meta-analyses, and explore new research domains. It is most effective for empirical fields involving experiments and concrete results.
Elicit cannot answer questions or provide information not contained within academic papers. It is less effective for retrieving specific facts (e.g., 'How many cars were sold in Malaysia last year?') or for research in theoretical or non-empirical domains.
Elicit searches a database of 125 million academic papers from the Semantic Scholar corpus, spanning all academic disciplines. When extracting data, Elicit utilizes the full text if available, or the abstract if the full text is not accessible.
As a general guideline, assume approximately 90% of the information provided by Elicit is accurate. It is essential to verify the output closely. Elicit facilitates this by citing the sources for all information generated by its language models.
Papers you upload to Elicit for analysis remain private and are not shared with any other parties.
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