Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

LitLLM: A Toolkit for Scientific Literature Review (2402.01788v1)

Published 2 Feb 2024 in cs.CL, cs.AI, and cs.IR
LitLLM: A Toolkit for Scientific Literature Review

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.

PDF HTML Abstract

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.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (44)
  1. Gradio: Hassle-free sharing and testing of ml models in the wild. arXiv preprint arXiv:1906.02569.
  2. LLMs for Literature Review generation: Are we there yet? Under submission.
  3. Exploring the boundaries of reality: Investigating the phenomenon of artificial intelligence hallucination in scientific writing through chatgpt references. Cureus, 15.
  4. Language models are few-shot learners.
  5. Agent instructs large language models to be general zero-shot reasoners. ArXiv, abs/2310.03710.
  6. Editeval: An instruction-based benchmark for text improvements. arXiv preprint arXiv:2209.13331.
  7. Retrieval-augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997.
  8. Michael Haman and Milan Školník. 2023. Using chatgpt to conduct a literature review. Accountability in Research, pages 1–3.
  9. Large language models are zero-shot rankers for recommender systems. ArXiv, abs/2305.08845.
  10. Jingshan Huang and Ming Tan. 2023. The role of chatgpt in scientific communication: writing better scientific review articles. American Journal of Cancer Research, 13(4):1148.
  11. A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232.
  12. Retrieval-augmented controllable review generation. In Proceedings of the 28th International Conference on Computational Linguistics, pages 2284–2295.
  13. The semantic scholar open data platform. arXiv preprint arXiv:2301.10140.
  14. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199–22213.
  15. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33:9459–9474.
  16. Do dall-e and flamingo understand each other? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1999–2010.
  17. Self-prompting large language models for zero-shot open-domain qa.
  18. The dawn after the dark: An empirical study on factuality hallucination in large language models. arXiv preprint arXiv:2401.03205.
  19. Xiang Lisa Li and Percy Liang. 2021. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190.
  20. Retrieval-augmented generation for code summarization via hybrid gnn. arXiv preprint arXiv:2006.05405.
  21. Llm comparative assessment: Zero-shot nlg evaluation through pairwise comparisons using large language models.
  22. S2ORC: The semantic scholar open research corpus. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4969–4983, Online. Association for Computational Linguistics.
  23. Multi-XScience: A large-scale dataset for extreme multi-document summarization of scientific articles. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8068–8074. Association for Computational Linguistics.
  24. Zero-shot listwise document reranking with a large language model. arXiv preprint arXiv:2305.02156.
  25. Retrieval augmented code generation and summarization. arXiv preprint arXiv:2108.11601.
  26. Rankvicuna: Zero-shot listwise document reranking with open-source large language models. arXiv preprint arXiv:2309.15088.
  27. Rankzephyr: Effective and robust zero-shot listwise reranking is a breeze! arXiv preprint arXiv:2312.02724.
  28. Guanghui Qin and Jason Eisner. 2021. Learning how to ask: Querying LMs with mixtures of soft prompts. arXiv preprint arXiv:2104.06599.
  29. Dynamic modality interaction modeling for image-text retrieval. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.
  30. Ehud Reiter and Robert Dale. 1997. Building applied natural language generation systems. Natural Language Engineering, 3(1):57–87.
  31. Prompt space optimizing few-shot reasoning success with large language models. ArXiv, abs/2306.03799.
  32. Wit: Wikipedia-based image text dataset for multimodal multilingual machine learning. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.
  33. Trainable sentence planning for complex information presentations in spoken dialog systems. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04), pages 79–86, Barcelona, Spain.
  34. Is chatgpt good at search? investigating large language models as re-ranking agent. arXiv preprint arXiv:2304.09542.
  35. Galactica: A large language model for science. arXiv preprint arXiv:2211.09085.
  36. Efficient retrieval augmented generation from unstructured knowledge for task-oriented dialog. arXiv preprint arXiv:2102.04643.
  37. A comprehensive survey of hallucination mitigation techniques in large language models. arXiv preprint arXiv:2401.01313.
  38. Finetuned language models are zero-shot learners. ArXiv, abs/2109.01652.
  39. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems.
  40. Coca: Contrastive captioners are image-text foundation models. Trans. Mach. Learn. Res., 2022.
  41. Rank-without-gpt: Building gpt-independent listwise rerankers on open-source large language models. arXiv preprint arXiv:2312.02969.
  42. Mamo: Fine-grained vision-language representations learning with masked multimodal modeling. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval.
  43. Large language models are human-level prompt engineers. arXiv preprint arXiv:2211.01910.
  44. Large language models for information retrieval: A survey. arXiv preprint arXiv:2308.07107.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Shubham Agarwal (34 papers)
  2. Issam H. Laradji (21 papers)
  3. Laurent Charlin (51 papers)
  4. Christopher Pal (97 papers)
Citations (10)
View on Semantic Scholar
Github Logo Streamline Icon: https://streamlinehq.com

GitHub

  1. GitHub - shubhamagarwal92/LitLLM: LitLLM: A Toolkit for Scientific Literature Review (42 stars)
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube