LitLLM: A Toolkit for Scientific Literature Review
(2402.01788)Abstract
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 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.
Overview
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The LitLLM toolkit is designed to improve scientific literature review processes by reducing factual inaccuracies using Retrieval Augmented Generation (RAG).
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It features a modular pipeline for automated generation of related work sections in scientific papers, enhanced by re-ranking search results and providing easy access on GitHub and Huggingface Space.
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The toolkit uses the Semantic Scholar API for paper retrieval and incorporates advanced LLM-based strategies, like zero-shot and plan-based generation, for creating customized reviews.
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Despite its advancements, the toolkit should be used responsibly with manual reviews to ensure factual accuracy, and future updates may include full-text analysis and additional search APIs.
Overview of LitLLM Toolkit
The LitLLM toolkit presents a significant advancement in the field of scientific literature review by addressing some of the prominent issues encountered with the use of LLMs in this context. Specifically, it combats the challenges of factual inaccuracies and oversight of recent studies, which were not part of the LLMs' original training data. The proposed toolkit leverages Retrieval Augmented Generation (RAG) to ensure that the literature reviews produced are based on factual content, thus reducing the instances of hallucinations commonly observed in LLM-generated texts.
Enhancements in Literature Review Generation
LitLLM operates through a multi-step modular pipeline that enables the automatic generation of related work sections for scientific papers. The process begins by summarizing user-provided abstracts into keywords, subsequently used for a web search to retrieve relevant papers. The system's re-ranking capability further sharpens the focus by selecting documents that closely align with the user's abstract. Based on these refined search results, a coherent related work section is generated. The open-source availability of the toolkit on GitHub and Huggingface Space, coupled with an instructional video, underscores the commitment to accessibility and user support.
Pipeline Design and Related Work
Diving into the mechanics of LitLLM, the paper retrieval module uses Semantic Scholar API to fetch documents, providing options for users to input additional keywords or reference papers to guide the search, thus enhancing precision and relevance. The re-ranking module is tasked with ordering the retrieved documents based on their relevance to the user-provided abstract. As for the final stage, the summary generation module utilizes LLM-based strategies, particularly zero-shot and plan-based generation, to construct the literature review. Plan-based generation is especially noteworthy as it appeals to authorial preference, providing customizable controls over the structure and content of the generated review.
Concluding Thoughts
The developed toolkit represents a stride forward in the application of LLMs for academic writing and research. The complexity of generating factually accurate and up-to-date related work sections in academic papers is adeptly managed by LitLLM, making it a potential mainstay in researcher toolkits. Nonetheless, the authors advocate for responsible usage, suggesting that outputs be meticulously reviewed to curb any residual factual inaccuracies. As for future directions, expansion to encompass full-text analysis and the integration of multiple academic search APIs are identified as logical next steps in the evolution of LitLLM. This progression aims at crafting more nuanced and contextually rich literature reviews, further enhancing the toolkit's capability as a research assistant.
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