AI and Generative AI for Research Discovery and Summarization

• URL: http://arxiv.org/abs/2401.06795v2
• Date: Tue, 26 Mar 2024 16:44:34 GMT
• Title: AI and Generative AI for Research Discovery and Summarization
• Authors: Mark Glickman, Yi Zhang,
• Abstract summary: AI and generative AI tools have burst onto the scene this year, creating incredible opportunities to increase work productivity and improve our lives. One area that these tools can make a substantial impact is in research discovery and summarization. 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.
• Score: 3.8601741392210434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Artificial intelligence to automate the systematic review of scientific literature [0.0]
  We present a survey of AI techniques proposed in the last 15 years to help researchers conduct systematic analyses of scientific literature. We describe the tasks currently supported, the types of algorithms applied, and available tools proposed in 34 primary studies.
  arXiv  Detail & Related papers  (2024-01-13T19:12:49Z)
• Generative AI in Writing Research Papers: A New Type of Algorithmic Bias and Uncertainty in Scholarly Work [0.38850145898707145]
  Large language models (LLMs) and generative AI tools present challenges in identifying and addressing biases. generative AI tools are susceptible to goal misgeneralization, hallucinations, and adversarial attacks such as red teaming prompts. We find that incorporating generative AI in the process of writing research manuscripts introduces a new type of context-induced algorithmic bias.
  arXiv  Detail & Related papers  (2023-12-04T04:05:04Z)
• Best uses of ChatGPT and Generative AI for computer science research [0.0]
  This paper explores the diverse applications of ChatGPT and other generative AI technologies in computer science academic research. We highlight innovative uses such as brainstorming research ideas, aiding in the drafting and styling of academic papers and assisting in the synthesis of state-of-the-art section.
  arXiv  Detail & Related papers  (2023-11-18T21:57:54Z)
• Can AI Serve as a Substitute for Human Subjects in Software Engineering Research? [24.39463126056733]
  This vision paper proposes a novel approach to qualitative data collection in software engineering research by harnessing the capabilities of artificial intelligence (AI) We explore the potential of AI-generated synthetic text as an alternative source of qualitative data. We discuss the prospective development of new foundation models aimed at emulating human behavior in observational studies and user evaluations.
  arXiv  Detail & Related papers  (2023-11-18T14:05:52Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration [39.96202614397779]
  Recent research has explored the potential for artificial intelligence to support and augment humans in data storytelling. There lacks a systematic review to understand data storytelling tools from the perspective of human-AI collaboration. This paper investigated existing tools with a framework from two perspectives: the stages in the storytelling workflow where a tool serves, including analysis, planning, implementation, and communication, and the roles of humans and AI.
  arXiv  Detail & Related papers  (2023-09-27T15:30:50Z)
• The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence [67.70415658080121]
  Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole. AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access. Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
  arXiv  Detail & Related papers  (2023-07-09T21:16:56Z)
• GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration [97.68234051078997]
  We discuss how Pyserini can be integrated with the Hugging Face ecosystem of open-source AI libraries and artifacts. We include a Jupyter Notebook-based walk through the core interoperability features, available on GitHub. We present GAIA Search - a search engine built following previously laid out principles, giving access to four popular large-scale text collections.
  arXiv  Detail & Related papers  (2023-06-02T12:09:59Z)
• LLM-based Interaction for Content Generation: A Case Study on the Perception of Employees in an IT department [85.1523466539595]
  This paper presents a questionnaire survey to identify the intention to use generative tools by employees of an IT company. Our results indicate a rather average acceptability of generative tools, although the more useful the tool is perceived to be, the higher the intention seems to be. Our analyses suggest that the frequency of use of generative tools is likely to be a key factor in understanding how employees perceive these tools in the context of their work.
  arXiv  Detail & Related papers  (2023-04-18T15:35:43Z)
• Artificial Intelligence in Concrete Materials: A Scientometric View [77.34726150561087]
  This chapter aims to uncover the main research interests and knowledge structure of the existing literature on AI for concrete materials. To begin with, a total of 389 journal articles published from 1990 to 2020 were retrieved from the Web of Science. Scientometric tools such as keyword co-occurrence analysis and documentation co-citation analysis were adopted to quantify features and characteristics of the research field.
  arXiv  Detail & Related papers  (2022-09-17T18:24:56Z)
• Learning from learning machines: a new generation of AI technology to meet the needs of science [59.261050918992325]
  We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data.
  arXiv  Detail & Related papers  (2021-11-27T00:55:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.