R Govys

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 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 statisticians.

Presenters:
Dr. Mark Glickman, a Fellow of the American Statistical Association, is Senior Lecturer on Statistics at the Harvard University Department of Statistics, and Senior Statistician at the Center for Healthcare Organization and Implementation Research, a Veterans Administration Center of Innovation.

Yi Zhang is a PhD student in the Department of Statistics at Harvard University. Her research interests focus on developing statistical methodologies for robust causal inference and data-driven decision-making in social and biomedical sciences.