Summary of Litllm: a Toolkit For Scientific Literature Review, by Shubham Agarwal et al.
LitLLM: A Toolkit for Scientific Literature Review
by Shubham Agarwal, Issam H. Laradji, Laurent Charlin, Christopher Pal
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel toolkit is proposed to generate automatic literature reviews using Retrieval Augmented Generation (RAG) principles and specialized prompting techniques with Large Language Models (LLMs). The system first retrieves relevant papers based on user-provided abstracts, then re-ranks the results, and finally generates a related work section. This approach reduces the time and effort required for literature reviews compared to traditional methods. The toolkit is open-source and available at GitHub and Huggingface space. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new tool helps scientists review research papers by using special computer models. These models look up relevant papers based on what you write about, then sort them in order of importance. Finally, the model creates a list of related studies. This makes it easier to find important information and saves time compared to doing things manually. |
Keywords
» Artificial intelligence » Prompting » Rag » Retrieval augmented generation
