What is scientific knowledge produced by Large Language Models?

Abstract

This article examines the nature of scientific knowledge generated by Large Language Models (LLMs) and assesses their impact on scientific discoveries and the philosophy of science. LLMs, such as GPT‑4, are advanced deep learning algorithms capable of performing various natural language processing tasks, including text generation, translation, and data analysis. The study aims to explore how these technologies influence the scientific research process, questioning the classification and validity of AI‑assisted scientific discoveries. The methodology involves a comprehensive review of existing literature on the application of LLMs in various scientific fields, coupled with an analysis of their ethical implications. Key findings highlight the benefits of LLMs, including accelerated research processes, enhanced accuracy, and the ability to integrate interdisciplinary knowledge. However, challenges such as issues of reliability, the ethical responsibility of AI‑generated content, and environmental concerns are also discussed. The paper concludes that while LLMs significantly contribute to scientific advancements, their use necessitates a reevaluation of traditional concepts in the philosophy of science and the establishment of new ethical guidelines to ensure transparency, accountability, and integrity in AI‑assisted research. This balanced approach aims to harness the potential of LLMs while addressing the ethical and practical challenges they present.