任务表述方式会影响大模型判断，不当表述易引发错误预设。
Impact of Task Phrasing on Presumptions in Large Language Models
- 用中性表述可减少模型预设倾向。
- 模型在推理时仍会受任务表述影响产生偏差。
- 适合关注模型安全与可控性的研究者阅读。
大语言模型在不可预测的实际应用中的安全性和可靠性问题促使本研究探讨任务表述如何导致模型产生预设，使其难以适应任务变化。以迭代囚徒困境为案例，实验发现，即使经过推理步骤，模型在任务表述引导下仍会形成预设。而当任务表述保持中立时，模型能展现更少预设的逻辑推理能力。结果表明，恰当的任务表述对降低模型预设风险至关重要。
原文摘要 · Abstract (English)
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 case study. Our experiments reveal that LLMs are susceptible to presumptions when making decisions even with reasoning steps. However, when the task phrasing was neutral, the models demonstrated logical reasoning without much presumptions. These findings highlight the importance of proper task phrasing to reduce the risk of presumptions in LLMs.
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