Impact of Task Phrasing on Presumptions in Large Language Models

ArXi:2605.00436v1 Announce Type: cross Concerns with the safety and reliability of applying large-language models (LLMs) in unpredictable real-world applications motivate this study, which examines how task phrasing can lead to presumptions in LLMs, making it difficult for them to adapt when the task deviates from these assumptions. We investigated the impact of these presumptions on the performance of LLMs using the iterated prisoner's dilemma as a. Our experiments reveal that LLMs are susceptible to presumptions when making decisions even with reasoning steps.