Customer data typically is held in database systems, which can be seen as
rule-based knowledge base, whereas businesses increasingly want to benefit from
the capabilities of large, pre-trained language models.
In this technical report, we describe a case study of how a commercial rule
engine and an integrated neural chatbot may be integrated, and what level of
control that particular integration mode leads to. We also discuss alternative
ways (including past ways realized in other systems) how researchers strive to
maintain control and avoid what has recently been called model "hallucination".

客户数据通常保存在数据库系统中，这些系统可以看作是基于规则的知识库，而企业越来越希望从大型预训练语言模型的能力中受益。本技术报告中，我们描述了一个商业规则引擎和一个集成的神经聊天机器人的案例研究，以及这种特定集成模式所带来的控制级别。我们还讨论了研究人员为保持控制和避免最近被称为模型 “幻觉” 的方式的替代方法（包括在其他系统中实现的过去方法）。

混合聊天机器人中的控制

Control in Hybrid Chatbots

Where early work on dialogue in Computational Linguistics put much emphasis
on dialogue structure and its relation to the mental states of the dialogue
participants (e.g., Allen 1979, Grosz & Sidner 1986), current work mostly
reduces dialogue to the task of producing at any one time a next utterance;
e.g. in neural chatbot or Visual Dialogue settings. As a methodological
decision, this is sound: Even the longest journey is a sequence of steps. It
becomes detrimental, however, when the tasks and datasets from which dialogue
behaviour is to be learned are tailored too much to this framing of the
problem. In this short note, we describe a family of settings which still allow
to keep dialogues simple, but add a constraint that makes participants care
about reaching mutual understanding. In such agreement games, there is a
secondary, but explicit goal besides the task level goal, and that is to reach
mutual understanding about whether the task level goal has been reached. As we
argue, this naturally triggers meta-semantic interaction and mutual engagement,
and hence leads to richer data from which to induce models.

本文介绍了一种通过协议达成共识的对话设置，除任务级别目标外，还有一个次要的、显式的目的 —— 达成对任务级别目标是否达成的共识 —— 使得会话参与者更关心彼此的理解，从而产生更丰富的数据以推导模型。