

Openscholar
Overview :
OpenScholar is a retrieval-augmented language model (LM) designed to assist scientists in effectively navigating and synthesizing scientific literature by first searching for relevant papers and then generating answers based on those sources. This model is significant for managing the millions of scientific papers published each year and helps scientists find the information they need or keep up with the latest discoveries in specific subfields.
Target Users :
Target audience includes scientists, researchers, and users who require in-depth analysis and synthesis of large quantities of scientific literature. OpenScholar enhances research efficiency and depth by aiding users in quickly finding the necessary information through the retrieval and synthesis of relevant scientific documents.
Use Cases
Researchers use OpenScholar to quickly find the latest research findings in specific fields.
Scientists leverage OpenScholar to synthesize multiple papers for writing review articles.
Academic institutions provide students with the latest academic resources and research updates through OpenScholar.
Features
? Retrieval enhancement: Answers questions by retrieving relevant scientific literature.
? Multi-paper tasks: Handles multiple papers to provide synthesized answers.
? Zero-shot learning: Supports inference in zero-shot learning scenarios.
? Feedback loops: Utilizes self-feedback loops during the generation process.
? Post-processing citation attribution: Attaches citation attribution after generation.
? Re-ranking model: Optimizes answer relevance using a re-ranking model.
? Semantic Scholar API: Integrates the Semantic Scholar API to enhance feedback results.
? Abstract usage: Considers abstracts to enhance re-ranking results.
How to Use
1. Ensure that all necessary libraries and environments are installed, such as Python 3.10.0 and the spacy model en_core_web_sm.
2. Set up the API key, for example, the Semantic Scholar API key.
3. Execute the OpenScholar inference, specifying the input file, model name, and other parameters via the command-line tool.
4. Utilize the provided retrieval script for offline searches, or combine the Semantic Scholar Paper API with web search APIs for retrieval.
5. Adjust configurations as needed, such as setting up feedback loops and post-processing of citation attribution.
6. Run the trained model to generate responses based on the retrieval results.
7. Analyze and utilize the generated responses for further research or literature reviews.
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