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Enhancing Pipeline-Based Conversational Agents with Large Language Models by Foosherian, Mina; Purwins, Hendrik; Rathnayake, Purna; Alam, Touhidul; Teimao, Rui; Thoben, Klaus-Dieter is a scholarly article available to read on EtoBox.

What is Enhancing Pipeline-Based Conversational Agents with Large Language Models about?

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. In 2) LLMs can assist in contextualization, intent classification to prevent conversational breakdown and handle out-of-scope questions, auto-correcting utterances, rephrasing responses, formulating disambiguation questions, summarization, and enabling closed question-answering capabilities. We conducted informal experiments with GPT-4 in the private banking domain to demonstrate the scenarios above with a practical example. Companies may be hesitant to replace their pipeline-based agents with LLMs entirely due to privacy concerns and the need for deep integration within their existing ecosystems. A hybrid approach in wh

Author

Foosherian, Mina; Purwins, Hendrik; Rathnayake, Purna; Alam, Touhidul; Teimao, Rui; Thoben, Klaus-Dieter

Published

2023

Language

EN