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 this https URL
and Huggingface space (this https URL)
with the video demo at this https URL

利用大型语言模型（LLM）的检索增强生成（RAG）原理和专门的提示与指示技术，我们提出了一个工具包来减少文献综述的时间和工作量，建立了我们的工具包作为一种高效的替代方法。