@inproceedings{chen-etal-2024-u,
title = "Are {U} a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor {LLM}",
author = "Chen, Yang and
Yang, Chong and
Hu, Tu and
Chen, Xinhao and
Lan, Man and
Cai, Li and
Zhuang, Xinlin and
Lin, Xuan and
Lu, Xin and
Zhou, Aimin",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.51",
pages = "878--890",
abstract = "Although large language models (LLMs) acquire extensive world knowledge and some reasoning abilities, their proficiency in generating humorous sentences remains a challenge. Previous research has demonstrated that the humor generation capabilities of ChatGPT are confined to producing merely 25 unique jokes. In this work, we concentrate on endowing LLMs with the ability of generating puns, a particular category of humor by preference learning method. We propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences. Specifically, we improve the Direct Preference Optimization (DPO) algorithm to address the challenge of multi-objective alignment problem. Besides, to facilitate further advancement in this field, we collect a Chinese Pun (ChinesePun) dataset, containing 2.1k puns and corresponding annotations. Experimental results on both Chinese and English benchmark datasets demonstrate that our method significantly outperforms all the baseline models.",
}
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<abstract>Although large language models (LLMs) acquire extensive world knowledge and some reasoning abilities, their proficiency in generating humorous sentences remains a challenge. Previous research has demonstrated that the humor generation capabilities of ChatGPT are confined to producing merely 25 unique jokes. In this work, we concentrate on endowing LLMs with the ability of generating puns, a particular category of humor by preference learning method. We propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences. Specifically, we improve the Direct Preference Optimization (DPO) algorithm to address the challenge of multi-objective alignment problem. Besides, to facilitate further advancement in this field, we collect a Chinese Pun (ChinesePun) dataset, containing 2.1k puns and corresponding annotations. Experimental results on both Chinese and English benchmark datasets demonstrate that our method significantly outperforms all the baseline models.</abstract>
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%0 Conference Proceedings
%T Are U a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor LLM
%A Chen, Yang
%A Yang, Chong
%A Hu, Tu
%A Chen, Xinhao
%A Lan, Man
%A Cai, Li
%A Zhuang, Xinlin
%A Lin, Xuan
%A Lu, Xin
%A Zhou, Aimin
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F chen-etal-2024-u
%X Although large language models (LLMs) acquire extensive world knowledge and some reasoning abilities, their proficiency in generating humorous sentences remains a challenge. Previous research has demonstrated that the humor generation capabilities of ChatGPT are confined to producing merely 25 unique jokes. In this work, we concentrate on endowing LLMs with the ability of generating puns, a particular category of humor by preference learning method. We propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences. Specifically, we improve the Direct Preference Optimization (DPO) algorithm to address the challenge of multi-objective alignment problem. Besides, to facilitate further advancement in this field, we collect a Chinese Pun (ChinesePun) dataset, containing 2.1k puns and corresponding annotations. Experimental results on both Chinese and English benchmark datasets demonstrate that our method significantly outperforms all the baseline models.
%U https://aclanthology.org/2024.findings-acl.51
%P 878-890
Markdown (Informal)
[Are U a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor LLM](https://aclanthology.org/2024.findings-acl.51) (Chen et al., Findings 2024)
ACL
- Yang Chen, Chong Yang, Tu Hu, Xinhao Chen, Man Lan, Li Cai, Xinlin Zhuang, Xuan Lin, Xin Lu, and Aimin Zhou. 2024. Are U a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor LLM. In Findings of the Association for Computational Linguistics ACL 2024, pages 878–890, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.