LitLLM: A Toolkit for Scientific Literature Review
<|reference_start|>LitLLM: A Toolkit for Scientific Literature Review: 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.<|reference_end|>
@article{agarwal2024litllm:,
    title={LitLLM: A Toolkit for Scientific Literature Review},
    author={Shubham Agarwal, Issam H. Laradji, Laurent Charlin, Christopher Pal},
    journal={arXiv preprint arXiv:2402.01788},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.01788},
    primaryClass={cs.CL cs.AI cs.IR}
}