为对话人工智能的透明度和控制设计仪表板

摘要

对话式LLM作为黑盒系统运作，让用户猜测为什么会看到特定的输出。这种缺乏透明度可能会带来问题，尤其是考虑到对偏见和真实性的担忧。为了解决这个问题，我们提出了一个端到端的原型，将可解释性技术与用户体验设计相结合，旨在使聊天机器人更加透明。我们首先展示了一个知名开源LLM存在“用户模型”的证据：通过检查系统的内部状态，我们可以提取与用户年龄、性别、教育水平和社会经济地位相关的数据。接下来，我们描述了一个仪表板的设计，该仪表板与聊天机器人界面相配合，实时显示这个用户模型。该仪表板还可以用于控制用户模型和系统的行为。最后，我们讨论了一个用户与被仪器化系统对话的研究。我们的结果表明，用户喜欢看到内部状态，这有助于他们揭示偏见行为并增强他们的控制感。参与者还提出了宝贵的建议，指向设计和机器学习研究的未来方向。我们的TalkTuner系统的项目页面和视频演示可在https://bit.ly/talktuner-project-page找到。

English

Conversational LLMs function as black box systems, leaving users guessing about why they see the output they do. This lack of transparency is potentially problematic, especially given concerns around bias and truthfulness. To address this issue, we present an end-to-end prototype-connecting interpretability techniques with user experience design-that seeks to make chatbots more transparent. We begin by showing evidence that a prominent open-source LLM has a "user model": examining the internal state of the system, we can extract data related to a user's age, gender, educational level, and socioeconomic status. Next, we describe the design of a dashboard that accompanies the chatbot interface, displaying this user model in real time. The dashboard can also be used to control the user model and the system's behavior. Finally, we discuss a study in which users conversed with the instrumented system. Our results suggest that users appreciate seeing internal states, which helped them expose biased behavior and increased their sense of control. Participants also made valuable suggestions that point to future directions for both design and machine learning research. The project page and video demo of our TalkTuner system are available at https://bit.ly/talktuner-project-page

为对话人工智能的透明度和控制设计仪表板

Designing a Dashboard for Transparency and Control of Conversational AI

摘要

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