Speaker
Description
The adaptation of large-language models (LLMs) like ChatGPT for information retrieval from scientific data, software and publications is offering new opportunities to simplify access to and understanding of science for persons from all levels of expertise. They can become tools to both enhance the usability of the open science environment we are building as well as help to provide systematic insight to a long-built corpus of scientific publications.
In this contribution, the uptake of Retrieval Augmented Generation (RAG)-enhanced chat applications in the construction of the open science environment of the KM3NeT neutrino detectors will be used as a focus point to explore and exemplify prospects for the wider application of LLMs for our science.
