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arXiv 2402.08030cs.HCcs.AIcs.LG

为什么以及何时基于LLM的助手会出错：调查基于提示词的交互在软件求助中的有效性

Why and When LLM-Based Assistants Can Go Wrong: Investigating the Effectiveness of Prompt-Based Interactions for Software Help-Seeking

Simon Fraser University（西蒙菲莎大学）；Stanford University（斯坦福大学）

Anjali Khurana, Hari Subramonyam, Parmit K Chilana

备注：
Accepted for publication in the Proceedings of the 29th International Conference on Intelligent User Interfaces (IUI'24), March 18--21, 2024, in Greenville, SC, USA
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AI总结：

通过16人实验对比基线LLM与SoftAIBot，发现提示词指南和领域上下文未能显著改善用户感知与任务完成率，强调需在LLM中引入可解释的上下文感知线索以提升软件求助效用。

摘要：

大型语言模型（LLM）助手，如ChatGPT，已成为帮助用户导航复杂、功能丰富的软件的搜索方法的潜在替代方案。LLM利用来自特定领域文本、软件手册和代码存储库的大量训练数据来模仿类似人类的交互，提供量身定制的帮助，包括分步说明。在这项工作中，我们通过一项16名参与者参与的受试者内实验和后续访谈，调查了LLM生成的软件指导。我们将基线LLM助手与针对特定软件上下文优化的LLM、即SoftAIBot进行了比较，后者还提供了构建适当提示词的指南。我们评估了任务完成情况、感知准确性、相关性和信任度。令人惊讶的是，尽管SoftAIBot的表现优于基线LLM，但我们的结果显示，无论有无提示词指南以及领域上下文的整合，LLM的使用和用户感知均无显著差异。大多数用户难以理解提示词文本如何与LLM的响应相关联，并且经常逐字遵循LLM的建议，即使这些建议是不正确的。这导致在使用LLM的建议进行软件任务时出现困难，从而导致较低的任务完成率。我们的详细分析还显示，用户未能察觉LLM响应中的不准确之处，表明他们缺乏软件专业知识与其评估LLM帮助的能力之间存在差距。随着设计特定领域LLM助手的推动力不断增强，我们强调将可解释的、上下文感知的线索纳入LLM的重要性，以帮助用户理解基于提示词的交互、识别偏见，并最大化LLM助手的效用。

英文摘要：

Large Language Model (LLM) assistants, such as ChatGPT, have emerged as potential alternatives to search methods for helping users navigate complex, feature-rich software. LLMs use vast training data from domain-specific texts, software manuals, and code repositories to mimic human-like interactions, offering tailored assistance, including step-by-step instructions. In this work, we investigated LLM-generated software guidance through a within-subject experiment with 16 participants and follow-up interviews. We compared a baseline LLM assistant with an LLM optimized for particular software contexts, SoftAIBot, which also offered guidelines for constructing appropriate prompts. We assessed task completion, perceived accuracy, relevance, and trust. Surprisingly, although SoftAIBot outperformed the baseline LLM, our results revealed no significant difference in LLM usage and user perceptions with or without prompt guidelines and the integration of domain context. Most users struggled to understand how the prompt's text related to the LLM's responses and often followed the LLM's suggestions verbatim, even if they were incorrect. This resulted in difficulties when using the LLM's advice for software tasks, leading to low task completion rates. Our detailed analysis also revealed that users remained unaware of inaccuracies in the LLM's responses, indicating a gap between their lack of software expertise and their ability to evaluate the LLM's assistance. With the growing push for designing domain-specific LLM assistants, we emphasize the importance of incorporating explainable, context-aware cues into LLMs to help users understand prompt-based interactions, identify biases, and maximize the utility of LLM assistants.

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。

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