mlsanityData to 13 Oct 2026

Automated Review Generation Method Based on Large Language Models

arXiv 2407.20906 ↗First posted 30 Jul 2024cs.CL cs.AI physics.data-ancited by 7

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Abstract (arXiv)

Literature research, vital for scientific work, faces the challenge of surging information volumes exceeding researchers' processing capabilities. We present an automated review generation method based on large language models (LLMs) to overcome efficiency bottlenecks and reduce cognitive load. Our statistically validated evaluation framework demonstrates that the generated reviews match or exceed manual quality, offering broad applicability across research fields without requiring users' domain knowledge. Applied to propane dehydrogenation (PDH) catalysts, our method swiftly analyzed 343 articles, averaging seconds per article per LLM account, producing comprehensive reviews spanning 35 topics, with extended analysis of 1041 articles providing insights into catalysts' properties. Through multi-layered quality control, we effectively mitigated LLMs' hallucinations, with expert verification confirming accuracy and citation integrity while demonstrating hallucination risks reduced to below 0.5% with 95% confidence. Released Windows application enables one-click review generation, enhancing research productivity and literature recommendation efficiency while setting the stage for broader scientific explorations.

Key facts. Cited by 7 papers in the map (data through 13 Oct 2026). Builds on 40 of its 81 references that are in the map. First posted on arXiv 30 Jul 2024.

Builds on

40 of 81 references.

References · 40

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,221
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,492
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  12. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 764
28 more
  1. 2022Galactica: A Large Language Model for Sciencecited by 741
  2. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 697
  3. 2023A Comprehensive Overview of Large Language Modelscited by 644
  4. 2024Hallucination is Inevitable: An Innate Limitation of Large Language Modelscited by 331
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  12. 2024RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculturecited by 81
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  14. 2023Split and Merge: Aligning Position Biases in LLM-based Evaluatorscited by 71
  15. 2023Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarizationcited by 70
  16. 2024LitLLM: A Toolkit for Scientific Literature Reviewcited by 61
  17. 2020Multi-document Summarization via Deep Learning Techniques: A Surveycited by 58
  18. 2023Summarization is (Almost) Deadcited by 44
  19. 2024ChatCite: LLM Agent with Human Workflow Guidance for Comparative Literature Summarycited by 30
  20. 2023A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrologycited by 26
  21. 2024Large Language Models as Evaluators for Recommendation Explanationscited by 19
  22. 2023SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generationcited by 17
  23. 2024System for systematic literature review using multiple AI agents: Concept and an empirical evaluationcited by 13
  24. 2021MOFSimplify: Machine Learning Models with Extracted Stability Data of Three Thousand Metal-Organic Frameworkscited by 11
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  28. 2024LLAssist: Simple Tools for Automating Literature Review Using Large Language Modelscited by 4

Built on by

Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 4; 2026: 3.

7 papers.

  1. 2025ReviewGuard: Enhancing Deficient Peer Review Detection via LLM-Driven Data Augmentationcited by 3
  2. 2025Reshaping MOFs text mining with a dynamic multi-agents framework of large language modelcited by 2
  3. 2025ARISE: Agentic Rubric-Guided Iterative Survey Engine for Automated Scholarly Paper Generationcited by 2
  4. 2026Talk is (Not) Cheap: A Taxonomy and Benchmark Coverage Audit for LLM Attackscited by 1
  5. 2025SciGPT: A Large Language Model for Scientific Literature Understanding and Knowledge Discoverycited by 0
  6. 2026AI-Model Network: Concept, Current State and Futurecited by 0
  7. 2026SurveyAgent-HKA: A multi-agent framework for scientific survey generation with LLMs and human knowledge augmentationcited by 0