Thanks to rapid progress in artificial intelligence, we have entered an era when technology and philosophy intersect in interesting ways. Sitting squarely at the centre of this intersection are large language models (LLMs). The more adept LLMs become at mimicking human language, the more vulnerable we become to anthropomorphism, to seeing the systems in which they are embedded as more human-like than they really are. This trend is amplified by the natural tendency to use philosophically loaded terms, such as "knows", "believes", and "thinks", when describing these systems. To mitigate this trend, this paper advocates the practice of repeatedly stepping back to remind ourselves of how LLMs, and the systems of which they form a part, actually work. The hope is that increased scientific precision will encourage more philosophical nuance in the discourse around artificial intelligence, both within the field and in the public sphere.

翻译：得益于人工智能的飞速发展，我们已进入一个技术与哲学以有趣方式相互交织的时代。大语言模型正位于这一交汇的核心。大语言模型模仿人类语言的能力越强，我们就越容易陷入拟人化倾向，将这些模型嵌入的系统视为比实际更接近人类。这种趋势因描述这些系统时自然使用“知道”“相信”“思考”等富含哲学内涵的术语而加剧。为减缓这一趋势，本文倡导反复退后一步，提醒自己大语言模型及其所构成系统的实际运作机制。其希望在于，通过提高科学精确性，能在人工智能领域内外的讨论中鼓励更多哲学层面的细腻思考。