Speaker
Description
Large Language Models (LLMs) have emerged as a transformative tool in society and are steadily working their way into scientific workflows. Despite their known tendency to hallucinate, rendering them perhaps unsuitable for direct scientific pipelines, LLMs excel in text-related tasks, offering a unique solution to manage the overwhelming volume of information presented at large conferences such as ACAT, ICHEP, and CHEP. This poster presents an innovative open-source application that harnesses the capabilities of an LLM to rank conference abstracts based on a user’s specified interests. By providing a list of interests to the LLM, it can sift through a multitude of abstracts, identifying those most relevant to the user, effectively helping to tailor the conference experience. The LLM, in this context, serves an assistant role, aiding conference attendees in navigating the deluge of information typical of large conferences. The poster will detail the workings of this application, provide prompts to optimize its use, and discuss potential future directions for this type of application.
