Conversational AIs, or chatbots, mimic human speech when conversing. Smart
assistants facilitate the automation of several tasks that needed human
intervention earlier. Because of their accuracy, absence of dependence on human
resources, and accessibility around the clock, chatbots can be employed in
vehicles too. Due to people's propensity to divert their attention away from
the task of driving while engaging in other activities like calling, playing
music, navigation, and getting updates on the weather forecast and latest news,
road safety has declined and accidents have increased as a result. It would be
advantageous to automate these tasks using voice commands rather than carrying
them out manually. This paper focuses on the development of a voice-based smart
assistance application for vehicles based on the RASA framework. The smart
assistant provides functionalities like navigation, communication via calls,
getting weather forecasts and the latest news updates, and music that are
completely voice-based in nature.

基于 RASA 框架的语音智能助理开发，用于车辆导航、通信、天气预报、新闻更新和音乐播放等任务。

基于 RASA 的车载语音智能助手系统

Voice-Based Smart Assistant System for Vehicles using RASA

Although conversational AIs have demonstrated fantastic performance, they
often generate incorrect information, or hallucinations. Retrieval augmented
generation has emerged as a promising solution to reduce these hallucinations.
However, these techniques still cannot guarantee correctness. Focusing on
question answering, we propose a framework that can provide statistical
guarantees for the retrieval augmented question answering system by combining
conformal prediction and global testing. In addition, we use Bayesian
optimization to choose hyperparameters of the global test to maximize the
performance of the system. Our empirical results on the Natural Questions
dataset demonstrate that our method can provide the desired coverage guarantee
while minimizing the average prediction set size.

对话人工智能存在生成不正确信息或产生幻觉的问题，本研究提出了一种结合了符合预测和全局测试的统计保证的检索增强问答系统，并使用贝叶斯优化来选择全局测试的超参数，通过在自然问题数据集上验证结果表明该方法能够提供所需的覆盖保证并最小化平均预测集大小。

可信的增强检索对话机器人

TRAC: Trustworthy Retrieval Augmented Chatbot

Off-Policy reinforcement learning has been a driving force for the
state-of-the-art conversational AIs leading to more natural humanagent
interactions and improving the user satisfaction for goal-oriented agents.
However, in large-scale commercial settings, it is often challenging to balance
between policy improvements and experience continuity on the broad spectrum of
applications handled by such system. In the literature, off-policy evaluation
and guard-railing on aggregate statistics has been commonly used to address
this problem. In this paper, we propose a method for curating and leveraging
high-precision samples sourced from historical regression incident reports to
validate, safe-guard, and improve policies prior to the online deployment. We
conducted extensive experiments using data from a real-world conversational
system and actual regression incidents. The proposed method is currently
deployed in our production system to protect customers against broken
experiences and enable long-term policy improvements.

本文提出了一种基于历史回归事故报告的高精度数据样本的培育和利用方法，以在在线部署之前验证、保护并改进政策，解决 Off-Policy 强化学习在大规模商业设置中难以平衡政策改进和经验连续性的问题，并提高了对话系统的用户满意度。