Introduction
In today’s digital age, researchers are constantly seeking ways to enhance their workflows and streamline their processes. This is where Elicit.org comes into play. Elicit is a powerful research assistant that harnesses the capabilities of language models like GPT-3 to automate various aspects of researchers’ tasks. Whether you are a student, an academic researcher, or an independent scholar, Elicit can revolutionize the way you approach your research endeavors. In this article, we will delve into the functionalities and benefits of Elicit, highlighting how it can significantly enhance your research efficiency and productivity.
What is Elicit?
Elicit.org is an innovative platform that utilizes advanced language models like GPT-3 to assist researchers in automating key elements of their workflows. One of the primary focuses of Elicit is the literature review process. By leveraging the power of GPT-3, Elicit can provide researchers with a user-friendly table containing relevant papers and summaries of crucial information extracted from those papers. This enables researchers to quickly access pertinent resources and gain valuable insights to support their work.
How do people use Elicit?
Since its inception, Elicit has garnered a substantial user base, primarily comprising researchers in academia, independent organizations, and individual researchers. The platform has proven to be invaluable for researchers seeking papers to cite and defining the direction of their research. Users have reported using Elicit to discover initial leads for papers, obtain answers to their questions, and even achieve exceptional scores on examinations. Notably, one researcher utilized a combination of Elicit’s Literature Review, Rephrase, and Summarization tasks to compile a comprehensive literature review for publication.
To gain a deeper understanding of how people are utilizing Elicit, one can explore their Twitter page, which showcases numerous examples of researcher feedback and different workflows employed. Additionally, their YouTube channel offers insightful demonstrations of various use cases and workflows that users can explore and implement in their own research.
The Elicit Literature Review Workflow
The Elicit Literature Review workflow offers researchers a multitude of features designed to enhance their research capabilities. Let’s explore some of the key functionalities that make Elicit stand out:
Find relevant papers using semantic similarity
Elicit employs semantic similarity, allowing researchers to discover relevant papers even if they do not precisely match the entered keywords. For instance, if a query mentions “mindfulness,” Elicit can return papers about “meditation” due to their semantic relationship. This broadens the scope of research and ensures comprehensive results.
Combine semantic similarity and keyword matching
Elicit provides researchers with the best of both worlds by offering a combination of semantic similarity and keyword matching. Researchers can cast a wide net to gather a broad range of papers and then zoom in on specific domains or keywords for more focused exploration.
Summaries of abstracts specific to your query
For every search result, Elicit generates custom summaries by reading the abstracts and extracting information relevant to the researcher’s query. This functionality sets Elicit apart from other tools
that lack summarization capabilities. The summaries offer researchers a preliminary understanding of the research, simplify complex abstracts, and aid in assessing the relevance of papers to their work.
Expanding search through the citation graph
When researchers star results in Elicit, the platform explores the citation graph associated with those papers to uncover additional relevant papers. This entails examining earlier papers referenced in the selected papers and subsequent papers that cited them. While some search tools possess similar capabilities, Elicit distinguishes itself by re-ranking the results based on semantic relevance to the researcher’s query and providing summaries of the resulting papers.
Customizable paper information and organization
Elicit allows researchers to add additional information about the papers column by column, enabling them to organize papers based on specific criteria. Researchers can access details such as population and intervention specifics, study results, publishing journals, and study types. Moreover, researchers can even pose entirely new questions about the papers. Once the columns are established, papers can be sorted based on various criteria, such as most cited, most recent, or largest sample size.
Filtering based on study type
Elicit offers the ability to filter papers based on study types, allowing researchers to focus on specific categories such as randomized controlled trials, meta-analyses, systematic reviews, or other review types. Combining filters and starring papers enables researchers to identify papers cited in systematic reviews or subsequent systematic reviews that reference a particular paper.
Saving and exporting work
Elicit provides researchers with the convenience of saving their work through the “Starred” page, where queries with starred results are stored for later review. Researchers can also download a CSV or .bib file, which can be imported into reference managers like Zotero, ensuring seamless integration with existing research workflows.
How is Elicit built?
Elicit is an evolving product that continuously receives updates and improvements, ensuring it remains at the forefront of research assistant technologies. As of April 2022, the Literature Review workflow within Elicit is implemented as follows:
- Semantic Similarity Search: When a user enters a question or query, Elicit performs a semantic search within its extensive database. This involves searching a corpus of over 115 million papers from the Semantic Scholar Academic Graph dataset. Elicit’s search algorithm identifies papers that are semantically similar to the user’s query, going beyond exact keyword matching. This allows researchers to discover relevant papers even if their queries do not precisely match the keywords.
- Relevance Ranking: Elicit takes the initial search results and applies a multi-step relevance ranking process. First, the platform utilizes a powerful GPT-3 Babbage model to re-rank the initial set of 400 papers. Then, the top 20 papers are further re-ranked using the castorini/monot5-base-msmarco-10k model. This multi-step ranking approach ensures that the most relevant and meaningful papers are prioritized and presented to the user.
- PDF Retrieval and Parsing: Elicit goes the extra mile to provide users with access to full-text PDFs of open access papers. The platform utilizes resources like Unpaywall to identify open access PDFs available online. Once identified, Elicit employs the Grobid tool to parse the PDFs and extract the full-text content. This enables users to not only view the metadata but also access the complete text of the papers within the Elicit interface.
- Custom Summaries Generation: For each search result, Elicit generates custom summaries by reading and analyzing the abstracts of the papers. These summaries are tailored to the user’s query, providing a concise and relevant overview of the research findings. The custom summaries help researchers quickly evaluate the papers’ suitability for their specific needs, saving time and effort in the review process.
- Exploring the Citation Graph: Elicit offers a unique feature that allows researchers to delve deeper into the citation graph associated with the papers. When a user stars specific papers of interest, Elicit explores the citation relationships of those papers. This involves examining both the earlier papers referenced by the selected papers and the subsequent papers that cited them. By traversing the citation graph, Elicit uncovers additional relevant papers, providing researchers with a comprehensive network of related research.
- Customizable Information and Organization: Elicit enables researchers to customize the information displayed for each paper. Researchers can add additional columns to showcase specific details such as population and intervention specifics, study results, publishing journals, and study types. This flexibility allows users to organize and prioritize papers based on their unique requirements and research criteria.
- Filters and Sorting: Elicit empowers researchers with powerful filtering capabilities. Users can apply filters based on study types, such as randomized controlled trials, meta-analyses, or systematic reviews. These filters help researchers narrow down their focus and locate papers relevant to their specific study design and methodology. Additionally, Elicit allows users to sort papers based on various criteria, such as citation count, recency, or sample size, providing further flexibility in organizing and prioritizing their research materials.
- Saving and Exporting: Elicit offers researchers the convenience of saving their work for future reference. The “Starred” page serves as a repository for queries with starred results, enabling users to review and revisit important papers. Additionally, researchers can export their work in a CSV or .bib file format, allowing seamless integration with popular reference managers like Zotero.
Limitations of Elicit
While Elicit offers an array of powerful features and functionalities, it is essential to acknowledge its limitations. These limitations provide researchers with a calibrated understanding of Elicit’s capabilities and ensure they exercise caution when relying on its outputs. Some of the limitations include:
- Language Model Limitations: Elicit relies on language models, such as GPT-3, which have been developed since 2019. While these models are already quite useful, they are not “Artificial General Intelligence” capable of completely replacing human researchers. It is crucial to recognize that language models are still evolving and have their own limitations.
- Faithfulness to Text: Language models like GPT-3 are not explicitly trained to faithfully represent the content of a given text. In order to ensure accurate summaries and extractions, Elicit has customized the models. However, there are cases where the model’s outputs may not fully capture the nuanced details of a paper or may misinterpret certain aspects. Elicit aims to prioritize accuracy, but there is still room for improvement in faithfully representing the content of research papers.
- Beta Development Status: Elicit is an early-stage tool that is continuously evolving based on user feedback and ongoing development efforts. While launching features in a beta state allows for rapid iteration, it also implies that the tool may still have certain rough edges and room for refinement. Users should bear in mind that Elicit-generated content is typically around 80-90% accurate, rather than 100% accurate.
- Quality Evaluation of Papers: Elicit currently lacks the capability to evaluate the quality or trustworthiness of individual papers. While researchers generally exercise caution and rigor in their work, there can be instances of questionable methodology or even research misconduct. Elicit does not possess the ability to differentiate between more reliable and less reliable papers, aside from providing certain heuristics such as citation count, journal reputation, and methodological details. It is crucial for researchers to exercise their own judgment and critical thinking when evaluating the quality of papers.
- Domain-Specific Focus: Elicit has primarily focused on empirical research, particularly in disciplines such as social sciences and biomedicine. While the long-term goal is to expand into other domains and types of research, it is important to acknowledge that Elicit’s current capabilities are best suited for empirical research and may not cover all domains comprehensively. Researchers working in specialized or niche areas may find that Elicit’s results may be more limited in scope.
The Team behind Elicit
Elicit is developed by Ought, a non-profit machine learning research lab comprising a team of eight individuals distributed across the Bay Area, Austin, New York, and Oristà. The Ought team brings together diverse experiences from academia, mature tech companies, and startups. Funding for Ought is sourced from organizations such as Open Philanthropy, Jaan Tallin, Future of Life Institute, and other individuals aligned with the effective altruism and long-termism communities. The mission of Ought and its team is to ensure the responsible and beneficial deployment of artificial intelligence in various domains, including research. Presently, Elicit stands as Ought’s flagship project.
Conclusion
Elicit.org has emerged as a groundbreaking research assistant that empowers researchers by automating and streamlining critical aspects of their workflows. By harnessing the power of language models like GPT-3, Elicit offers researchers the ability to conduct comprehensive literature reviews, discover relevant papers, and extract key information efficiently. With its user-friendly interface and advanced features, Elicit is revolutionizing the research landscape, enabling researchers to save time, enhance productivity, and achieve new levels of excellence. Embrace the power of Elicit and unlock the full potential of your research endeavors.
Start your journey with Elicit today and experience a new level of research efficiency and productivity. Visit https://elicit.org to learn more!