大型语言模型产生的科学知识是什么?

P. N. Baryshnikov
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引用次数: 0

摘要

本文探讨了大型语言模型(LLM)生成的科学知识的性质,并评估了它们对科学发现和科学哲学的影响。GPT-4 等 LLM 是先进的深度学习算法,能够执行各种自然语言处理任务,包括文本生成、翻译和数据分析。本研究旨在探讨这些技术如何影响科学研究过程,质疑人工智能辅助科学发现的分类和有效性。研究方法包括全面回顾有关 LLM 在各个科学领域应用的现有文献,并分析其伦理意义。主要研究结果强调了 LLMs 的好处,包括加快研究进程、提高准确性和整合跨学科知识的能力。不过,论文也讨论了一些挑战,如可靠性问题、人工智能生成内容的伦理责任以及环境问题。本文的结论是,尽管 LLM 对科学进步做出了重大贡献,但使用 LLM 需要重新评估科学哲学中的传统概念,并制定新的伦理准则,以确保人工智能辅助研究的透明度、问责制和完整性。这种平衡的方法旨在利用 LLMs 的潜力,同时应对它们带来的伦理和实际挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What is scientific knowledge produced by Large Language Models?
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.
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