Small Talk, Big Impact: The Energy Cost of Thanking AI
This addresses the problem of energy inefficiency in LLM interactions for developers and users, providing actionable insights for sustainable AI deployment, though it is incremental as it applies existing methods to a new context.
The paper quantified the energy cost of polite messages like 'thank you' in interactions with large language models, finding that input length, output length, and model size significantly affect energy use, with implications for billions of daily prompts.
Being polite is free - or is it? In this paper, we quantify the energy cost of seemingly innocuous messages such as ``thank you'' when interacting with large language models, often used by users to convey politeness. Using real-world conversation traces and fine-grained energy measurements, we quantify how input length, output length and model size affect energy use. While politeness is our motivating example, it also serves as a controlled and reproducible proxy for measuring the energy footprint of a typical LLM interaction. Our findings provide actionable insights for building more sustainable and efficient LLM applications, especially in increasingly widespread real-world contexts like chat. As user adoption grows and billions of prompts are processed daily, understanding and mitigating this cost becomes crucial - not just for efficiency, but for sustainable AI deployment.