Is the 'Calculator for Words' analogy useful for communicating about LLMs?

Large language models (LLMs) are fundamentally different from search engines, functioning more as 'vibe-machines' than information retrieval systems. However, conveying appropriate expectations and usage modes for these novel interfaces remains challenging. This paper critically examines Willison's 'calculator for words' analogy and Bucci's counter-arguments, analysing the strengths and limitations of this metaphor.

While we argue that the 'calculator for words' analogy serves as an effective negative heuristic -- discouraging users from treating generative AI prompts as search engine queries -- it falls short in providing positive intuition for effective LLM utilisation. To address this limitation, we propose a novel conceptual framework: 'maps of no territory.'

Drawing inspiration from Borges' 'On Exactitude in Science,' our 'maps of no territory' analogy aims to provide more nuanced intuitions for general audiences, guiding them towards effective use while steering them away from problematic applications. This metaphor offers a more comprehensive understanding of LLMs' nature, capabilities, and limitations, potentially fostering more informed and responsible engagement with these powerful AI systems.