LitLLM: A Toolkit for Scientific Literature Review
Shubham Agarwal Issam H. Laradji Laurent Charlin Christopher Pal
Conducting literature reviews for scientific papers is essential for
understanding research, its limitations, and building on existing work. It is a
tedious task which makes an automatic literature review generator appealing.
Unfortunately, many existing works that generate such reviews using Large
Language Models (LLMs) have significant limitations. They tend to
hallucinate-generate non-actual information-and ignore the latest research they
have not been trained on. To address these limitations, we propose a toolkit
that operates on Retrieval Augmented Generation (RAG) principles, specialized
prompting and instructing techniques with the help of LLMs. Our system first
initiates a web search to retrieve relevant papers by summarizing user-provided
abstracts into keywords using an off-the-shelf LLM. Authors can enhance the
search by supplementing it with relevant papers or keywords, contributing to a
tailored retrieval process. Second, the system re-ranks the retrieved papers
based on the user-provided abstract. Finally, the related work section is
generated based on the re-ranked results and the abstract. There is a
substantial reduction in time and effort for literature review compared to
traditional methods, establishing our toolkit as an efficient alternative. Our
open-source toolkit is accessible at https://github.com/shubhamagarwal92/LitLLM
and Huggingface space (https://huggingface.co/spaces/shubhamagarwal92/LitLLM)
with the video demo at https://youtu.be/E2ggOZBAFw0.