mlsanityData to 7 Oct 2026

AI and Generative AI for Research Discovery and Summarization

arXiv 2401.06795 ↗First posted 8 Jan 2024cs.CL cs.AI cs.LGcited by 4

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Abstract (arXiv)

AI and generative AI tools, including chatbots like ChatGPT that rely on large language models (LLMs), have burst onto the scene this year, creating incredible opportunities to increase work productivity and improve our lives. Statisticians and data scientists have begun experiencing the benefits from the availability of these tools in numerous ways, such as the generation of programming code from text prompts to analyze data or fit statistical models. One area that these tools can make a substantial impact is in research discovery and summarization. Standalone tools and plugins to chatbots are being developed that allow researchers to more quickly find relevant literature than pre-2023 search tools. Furthermore, generative AI tools have improved to the point where they can summarize and extract the key points from research articles in succinct language. Finally, chatbots based on highly parameterized LLMs can be used to simulate abductive reasoning, which provides researchers the ability to make connections among related technical topics, which can also be used for research discovery. We review the developments in AI and generative AI for research discovery and summarization, and propose directions where these types of tools are likely to head in the future that may be of interest to statistician and data scientists.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 8 Jan 2024.

Builds on

17 of 37 references.

References · 17

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
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  10. 2019Abductive Commonsense Reasoningcited by 300
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  12. 2015On Using Monolingual Corpora in Neural Machine Translationcited by 149
5 more
  1. 2020TLDR: Extreme Summarization of Scientific Documentscited by 136
  2. 2018The Importance of Being Recurrent for Modeling Hierarchical Structurecited by 42
  3. 2023Scientists' Perspectives on the Potential for Generative AI in their Fieldscited by 22
  4. 2023Mathematics, word problems, common sense, and artificial intelligencecited by 12
  5. 2023Abductive Reasoning with the GPT-4 Language Model: Case studies from criminal investigation, medical practice, scientific researchcited by 6

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Citing papers per year: 2025: 4.

4 papers.

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  1. 2025ResearchGPT: Benchmarking and Training LLMs for End-to-End Computer Science Research Workflows
  2. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
  3. 2025CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers
  4. 2025Deep Research: A Systematic Survey
  5. 2025The Empowerment of Science of Science by Large Language Models: New Tools and Methods
  6. 2024Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
  7. 2025The Budget AI Researcher and the Power of RAG Chains
  8. 2025Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation