LLM-based Frameworks for API Argument Filling in Task-Oriented Conversational Systems

TL;DR

This paper explores using Large Language Models for API argument filling in task-oriented conversational systems, introducing grounding techniques to improve their accuracy and proposing frameworks for better performance.

Contribution

It presents novel training and prompting frameworks that enable LLMs to effectively perform API argument filling through grounding techniques.

Findings

LLMs require grounding to perform argument filling effectively

Proposed frameworks significantly improve LLMs' argument filling accuracy

Grounded LLMs enable more reliable task-oriented dialogue systems

Abstract

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.