如何使用提示词
摘要
本文分析了过去两年中针对ChatGPT的提示词,发现提示逐渐转向间接和隐含指令,礼貌性减弱,表明用户正在适应LLMs的推理能力。
arXiv:2609.29657v1 Announce Type: new
Abstract: When users address large language models, they produce directive speech acts whose pragmatic features differ from those of both everyday conversation and traditional human-computer interaction, and these features change as users gain familiarity with the systems they address. This paper applies speech act and politeness theory to a corpus-pragmatic analysis of 2,000 English-language prompts drawn from publicly shared ChatGPT conversations, 1,000 from 2023 and 1,000 from 2025, using the ShareChat dataset. Each prompt is annotated for illocutionary force, directness, propositional content, and the presence of politeness markers, and the distribution of these features is compared across the two sampling years. The results show a consistent movement toward indirect, implicit, and fragmentary realizations of directive force, accompanied by a decline in politeness marking. The largest single change, a shift of 14.9 percentage points, occurs in propositional content, where explicit specification of the requested action gives way to implicit reliance on the system's inferential capacity, suggesting that users have updated their model of what the system can recover from reduced input, treating it as a competent implicature resolver. Rather than asking whether LLMs "really" understand language, we should ask: what kind of language have we created in learning to speak to them?
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# How To Do Things With Prompts Source: [https://arxiv.org/abs/2609.29657](https://arxiv.org/abs/2609.29657) [View PDF](https://arxiv.org/pdf/2609.29657) > Abstract:When users address large language models, they produce directive speech acts whose pragmatic features differ from those of both everyday conversation and traditional human\-computer interaction, and these features change as users gain familiarity with the systems they address\. This paper applies speech act and politeness theory to a corpus\-pragmatic analysis of 2,000 English\-language prompts drawn from publicly shared ChatGPT conversations, 1,000 from 2023 and 1,000 from 2025, using the ShareChat dataset\. Each prompt is annotated for illocutionary force, directness, propositional content, and the presence of politeness markers, and the distribution of these features is compared across the two sampling years\. The results show a consistent movement toward indirect, implicit, and fragmentary realizations of directive force, accompanied by a decline in politeness marking\. The largest single change, a shift of 14\.9 percentage points, occurs in propositional content, where explicit specification of the requested action gives way to implicit reliance on the system's inferential capacity, suggesting that users have updated their model of what the system can recover from reduced input, treating it as a competent implicature resolver\. Rather than asking whether LLMs "really" understand language, we should ask: what kind of language have we created in learning to speak to them? ## Submission history From: Kristina Šekrst \[[view email](https://arxiv.org/show-email/4ddb2b0f/2609.29657)\] **\[v1\]**Sat, 29 Aug 2026 18:18:24 UTC \(381 KB\)
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