Talking About Large Language Models

TL;DR

This paper discusses the philosophical and scientific challenges posed by large language models, emphasizing the need for clearer understanding to prevent anthropomorphism and promote nuanced discourse.

Contribution

It advocates for regular reflection on how LLMs function to foster scientific accuracy and philosophical clarity in AI discussions.

Findings

Increased scientific precision can reduce anthropomorphism.

Philosophical nuance improves with better understanding of LLMs.

Public discourse benefits from clearer explanations of AI capabilities.

Abstract

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.