Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2023

Abstract

Conversational systems are designed to offer human users social support or functional services through natural language interactions. Typical conversation researches mainly focus on the response-ability of the system, such as dialogue context understanding and response generation. In the era of large language models (LLMs), LLM-augmented conversational systems showcase exceptional capabilities of responding to user queries for different language tasks. However, as LLMs are trained to follow users' instructions, LLM-augmented conversational systems typically overlook the design of an essential property in intelligent conversations, i.e., goal awareness. In this tutorial, we will introduce the recent advances on the design of agent's awareness of goals in a wide range of conversational systems, including proactive, non-collaborative, and multi-goal conversational systems. In addition, we will discuss the main open challenges in developing agent's goal awareness in LLM-augmented conversational systems and several potential research directions for future studies.

Keywords

Conversational agents, Conversational information seeking, Conversational systems, Human users, Information seeking, Language model, Open-domain dialog, Proactivity, Task-oriented, Task-oriented dialog

Discipline

Databases and Information Systems | Information Security

Research Areas

Data Science and Engineering; Information Systems and Management

Areas of Excellence

Digital transformation

Publication

Proceedings of the 11th International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, Beijing, China, 2023 November 26-28

First Page

298

Last Page

301

ISBN

9798400704086

Identifier

10.1145/3624918.3629548

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3624918.3629548

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