Game-Theoretic Approach to Counter Dishonest LLM Providers

A recent study published on arXiv (2511.00847) introduces a game-theoretic approach aimed at tackling dishonest practices among Large Language Model (LLM) service providers. The researchers highlight weaknesses in API-based LLM services, where providers might covertly replace high-quality models with less expensive options or artificially enhance responses with irrelevant tokens to boost charges. This work presents the first formal economic model reflecting a realistic user-provider dynamic, allowing users to iteratively assign T queries to various providers who may act strategically. A key finding is the demonstration that for any epsilon in (0, 1/2) within a continuous strategy space, an approximate incentive-compatible mechanism exists with an additive approximation ratio. This research underscores the importance of ensuring honest service delivery as reliance on LLM APIs increases. The paper is categorized as a replace-cross type on arXiv.