Large Language Models in Research
I am interested in the applicability of large language models in science. This includes not only domain-specific language models and applications but also the impact on research and information science. Methodologically, chatbots have a significant influence on the methods of teaching and science communication. Libraries face special challenges in this context. From an information science and technical perspective, however, there are indeed exciting applications. Philosophical and scientific-theoretical questions are also being viewed in a new light. Can new knowledge emerge from a language model that represents the sum of what has already been described?
Large language models (LLMs) like GPT-4 are reshaping the landscape of scientific research, education, and information science. Their ability to understand, generate, and interact with text in natural language has opened up new avenues for both domain-specific applications and broader scientific inquiries. For instance, LLMs are being integrated into scientific workflows to assist with literature reviews, data analysis, and hypothesis generation, enhancing the efficiency and depth of research.
In education, LLMs are revolutionizing the methodology of teaching and science communication by providing personalized learning experiences and facilitating more effective engagement with complex scientific concepts. Their application extends to creating interactive educational content, automating feedback, and offering real-time assistance to students.
Libraries and information science professionals are navigating the dual challenges of leveraging LLMs for better information retrieval and management while also addressing concerns related to data privacy, misinformation, and ethical use. The integration of LLMs in library services promises to transform access to information, enabling more sophisticated search capabilities and personalized user interactions.
From a technical and information science perspective, LLMs offer novel applications in organizing and synthesizing vast amounts of data, potentially unveiling new insights and knowledge patterns that were previously difficult to discern. Moreover, the philosophical and epistemological implications of LLMs provoke critical discussions about the nature of knowledge and the potential of machines to generate new, original insights beyond the aggregation of existing information.
The question of whether LLMs can create new knowledge reflects a deeper inquiry into the capabilities of artificial intelligence in contributing to scientific advancement. While LLMs draw upon the vast pool of human-generated content, their ability to identify patterns, generate hypotheses, and even propose solutions presents a compelling case for their role in fostering innovation and expanding the frontiers of human knowledge. What I am still very enthusiastic about and what would have been unthinkable in this quality a few months ago, some paragraphs here have been translated with GPT from OpenAI or suggestions have been added according to a description.
Featured projects
I would like to highlight the following projects, as they are very exciting in the contexts mentioned.
Please note: All links lead to external websites. I have no connection to the projects and have no influence on their further development.
LLFLOW
An extensible, convenient, and efficient toolbox for finetuning large machine learning models, designed to be user-friendly, speedy and reliable, and accessible to the entire community.