On the Limits of Prompt-Conditioned Language Models as General-Purpose Learners

Large language models are often perceived as versatile problem solvers, but their capabilities are constrained by the limitations of language itself. This constraint arises from language being a condensed and capacity-restricted means of conveying task-related information. By modeling user-system interactions as a bilevel game, researchers have analyzed how tasks are encoded into prompts, revealing the inherent restrictions of language-based interfaces. The study suggests that the effectiveness of large language models is hindered by their reliance on prompt-conditioned inputs, which can lead to suboptimal performance in certain tasks¹. This finding has significant implications for the development and deployment of AI systems, as it highlights the need for more nuanced understanding of their capabilities and limitations. The constrained nature of language-based interfaces matters to practitioners, as it underscores the importance of carefully evaluating the suitability of large language models for specific tasks and considering alternative approaches when necessary.