Enhancing Pipeline-Based Conversational Agents with Large Language Models

Understand

The latest advancements in AI and deep learning have led to a breakthrough in large language model (LLM)-based agents such as GPT-4.

• However, many commercial conversational agent development tools are pipeline-based and have limitations in holding a human-like conversation.
• This paper investigates the capabilities of LLMs to enhance pipeline-based conversational agents during two phases: 1) in the design and development phase and 2) during operations.
• In 1) LLMs can aid in generating training data, extracting entities and synonyms, localization, and persona design.