Prompting and evaluating large language models for proactive dialogues: Clarification, target-guided, and non-collaboration
Conversational systems based on Large Language Models (LLMs), such as ChatGPT, show exceptional proficiency in context understanding and response generation. However, they still possess limitations, such as failing to ask clarifying questions to ambiguous queries or refuse users' unreasonable r...
Saved in:
Main Authors: | DENG, Yang, LIAO, Lizi, CHEN, Liang, WANG, Hongru, LEI, Wenqiang, CHUA, Tat-Seng |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2023
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/9116 https://ink.library.smu.edu.sg/context/sis_research/article/10119/viewcontent/Prompting.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Prompting and evaluating large language models for proactive dialogues: Clarification, target-guided, and non-collaboration
by: DENG, Yang, et al.
Published: (2023) -
Proactive conversational agents
by: LIAO, Lizi, et al.
Published: (2023) -
Towards human-centered proactive conversational agents
by: DENG, Yang, et al.
Published: (2024) -
Proactive conversational agents in the post-ChatGPT world
by: LIAO, Lizi, et al.
Published: (2023) -
Rethinking conversational agents in the era of large language models: Proactivity, non-collaborativity, and beyond
by: DENG, Yang, et al.
Published: (2023)