The landscape for building conversational interfaces (chatbots) has witnessed a paradigm shift with recent developments in generative Artificial Intelligence (AI) based Large Language Models (LLMs), such as ChatGPT by OpenAI (GPT3.5 and GPT4), Google's Bard, Large Language Model Meta AI (LLaMA), among others. In this paper, we analyze capabilities and limitations of incorporating such models in conversational interfaces for the telecommunication domain, specifically for enterprise wireless products and services. Using Cradlepoint's publicly available data for our experiments, we present a comparative analysis of the responses from such models for multiple use-cases including domain adaptation for terminology and product taxonomy, context continuity, robustness to input perturbations and errors. We believe this evaluation would provide useful insights to data scientists engaged in building customized conversational interfaces for domain-specific requirements.

翻译：构建对话界面（聊天机器人）的格局已因生成式人工智能（AI）大语言模型（LLMs）的最新发展而发生范式转变，例如OpenAI的ChatGPT（GPT3.5和GPT4）、谷歌的Bard、Meta AI的LLaMA等。本文分析了将这些模型融入通信领域对话界面（特别是企业无线产品与服务）的能力与局限。我们利用Cradlepoint公司公开数据进行实验，针对术语与产品分类的领域适配、上下文连贯性、对输入扰动和错误的鲁棒性等多个用例，对上述模型的响应进行了比较分析。我们相信，这一评估将为致力于构建特定领域定制化对话界面的数据科学家提供有价值的见解。