基于LLM的面向任务对话系统中的API参数填充框架
LLM-based Frameworks for API Argument Filling in Task-Oriented Conversational Systems
AI总结：
本文研究大语言模型在面向任务对话系统中API参数填充任务的应用，提出训练和提示框架以接地LLM响应，实验证明所提技术显著提升参数填充性能，为自动化参数填充框架奠定基础。
摘要：
面向任务的对话代理通过与用户交互并利用外部API来协助用户。典型的面向任务对话系统可分解为三个阶段：外部API选择、参数填充和响应生成。我们工作的重点是参数填充任务，该任务负责准确提供所选API所需的参数。在理解对话历史和预定义的API模式后，参数填充任务预期为外部API提供必要信息，以生成理想的代理动作。在本文中，我们研究了大语言模型（LLMs）在API参数填充问题上的应用。我们的初步调查显示，LLMs需要一个额外的接地过程来成功执行参数填充，这启发我们设计训练和提示框架来接地其响应。我们的实验结果表明，当与所提出的技术配对时，LLMs的参数填充性能显著提高，为构建自动化参数填充框架开辟了新途径。
英文摘要：
Task-orientated conversational agents interact with users and assist them via leveraging external APIs. A typical task-oriented conversational system can be broken down into three phases: external API selection, argument filling, and response generation. The focus of our work is the task of argument filling, which is in charge of accurately providing arguments required by the selected API. Upon comprehending the dialogue history and the pre-defined API schema, the argument filling task is expected to provide the external API with the necessary information to generate a desirable agent action. In this paper, we study the application of Large Language Models (LLMs) for the problem of API argument filling task. Our initial investigation reveals that LLMs require an additional grounding process to successfully perform argument filling, inspiring us to design training and prompting frameworks to ground their responses. Our experimental results demonstrate that when paired with proposed techniques, the argument filling performance of LLMs noticeably improves, paving a new way toward building an automated argument filling framework.
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。
