增强ChatGPT中的用户交互：表征并整合多个提示以解决问题
Enhancing User Interaction in ChatGPT: Characterizing and Consolidating Multiple Prompts for Issue Resolution
AI总结：
本研究通过分析686个Java和Python编程提示，识别出11个提示设计缺陷，并尝试将多个提示整合为一个，以提升ChatGPT响应效率，节省时间、降低成本并提高满意度。
摘要：
提示设计在塑造ChatGPT的有效性方面起着关键作用，影响着模型提取上下文准确响应的能力。因此，最优的提示构建对于最大化ChatGPT的效用和性能至关重要。然而，次优的提示设计可能需要迭代细化，因为不精确或模糊的指令可能导致ChatGPT产生不期望的响应。现有研究探索了几种提示模式和策略，以提高ChatGPT生成响应的相关性。然而，对于需要提交多个提示的约束条件的探索仍是一项未满足的尝试。在本研究中，我们的贡献有两方面。首先，我们试图揭示需要多次迭代的提示设计中的缺陷。具体而言，我们手动分析了686个为解决Java和Python编程语言相关问题而提交的提示，并识别出十一个提示设计缺陷（例如，缺少规格说明）。这种缺陷探索可以增强ChatGPT中单个提示的有效性。其次，我们尝试通过将多个提示整合为一个提示来重现ChatGPT的响应。我们可以完全整合具有四个缺陷（例如，缺少上下文）的提示，并部分整合具有三个缺陷（例如，附加功能）的提示。这一努力为用户提供了具体证据，以设计更优的提示来缓解这些缺陷。我们的研究结果和证据可以——（a）节省用户时间，（b）降低成本，以及（c）提高用户满意度。
英文摘要：
Prompt design plays a crucial role in shaping the efficacy of ChatGPT, influencing the model's ability to extract contextually accurate responses. Thus, optimal prompt construction is essential for maximizing the utility and performance of ChatGPT. However, sub-optimal prompt design may necessitate iterative refinement, as imprecise or ambiguous instructions can lead to undesired responses from ChatGPT. Existing studies explore several prompt patterns and strategies to improve the relevance of responses generated by ChatGPT. However, the exploration of constraints that necessitate the submission of multiple prompts is still an unmet attempt. In this study, our contributions are twofold. First, we attempt to uncover gaps in prompt design that demand multiple iterations. In particular, we manually analyze 686 prompts that were submitted to resolve issues related to Java and Python programming languages and identify eleven prompt design gaps (e.g., missing specifications). Such gap exploration can enhance the efficacy of single prompts in ChatGPT. Second, we attempt to reproduce the ChatGPT response by consolidating multiple prompts into a single one. We can completely consolidate prompts with four gaps (e.g., missing context) and partially consolidate prompts with three gaps (e.g., additional functionality). Such an effort provides concrete evidence to users to design more optimal prompts mitigating these gaps. Our study findings and evidence can - (a) save users time, (b) reduce costs, and (c) increase user satisfaction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
