@inproceedings{zhang-etal-2025-lightweight,
title = "A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models",
author = "Zhang, Chenyang and
Lin, Jiayi and
Tong, Haibo and
Hou, Bingxuan and
Zhang, Dongyu and
Li, Jialin and
Wang, Junli",
editor = "Cao, Trista and
Das, Anubrata and
Kumarage, Tharindu and
Wan, Yixin and
Krishna, Satyapriya and
Mehrabi, Ninareh and
Dhamala, Jwala and
Ramakrishna, Anil and
Galystan, Aram and
Kumar, Anoop and
Gupta, Rahul and
Chang, Kai-Wei",
booktitle = "Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025)",
month = may,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.trustnlp-main.35/",
doi = "10.18653/v1/2025.trustnlp-main.35",
pages = "542--551",
ISBN = "979-8-89176-233-6",
abstract = "Multi-Aspect Controllable Text Generation (MCTG) introduces fine-grained multiple constraints in natural language generation, i.e. control attributes in topics, sentiments, and detoxification.MCTG demonstrates application prospects for trustworthy generation of Large Language Models (LLMs) but is limited by generalization issues.Existing work exploits additional structures and strategies for solutions, requiring LLMs' modifications.To activate LLMs' MCTG ability, we propose a lightweight MCTG pipeline based on data augmentation and instruction tuning.We analyze aspect bias and correlations in traditional datasets and address these concerns with augmented control attributes and sentences.Augmented datasets are feasible for instruction tuning.We conduct experiments for various LLMs backbone and parameter sizes, demonstrating general effectiveness on MCTG performance."
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%0 Conference Proceedings
%T A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models
%A Zhang, Chenyang
%A Lin, Jiayi
%A Tong, Haibo
%A Hou, Bingxuan
%A Zhang, Dongyu
%A Li, Jialin
%A Wang, Junli
%Y Cao, Trista
%Y Das, Anubrata
%Y Kumarage, Tharindu
%Y Wan, Yixin
%Y Krishna, Satyapriya
%Y Mehrabi, Ninareh
%Y Dhamala, Jwala
%Y Ramakrishna, Anil
%Y Galystan, Aram
%Y Kumar, Anoop
%Y Gupta, Rahul
%Y Chang, Kai-Wei
%S Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025)
%D 2025
%8 May
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-233-6
%F zhang-etal-2025-lightweight
%X Multi-Aspect Controllable Text Generation (MCTG) introduces fine-grained multiple constraints in natural language generation, i.e. control attributes in topics, sentiments, and detoxification.MCTG demonstrates application prospects for trustworthy generation of Large Language Models (LLMs) but is limited by generalization issues.Existing work exploits additional structures and strategies for solutions, requiring LLMs’ modifications.To activate LLMs’ MCTG ability, we propose a lightweight MCTG pipeline based on data augmentation and instruction tuning.We analyze aspect bias and correlations in traditional datasets and address these concerns with augmented control attributes and sentences.Augmented datasets are feasible for instruction tuning.We conduct experiments for various LLMs backbone and parameter sizes, demonstrating general effectiveness on MCTG performance.
%R 10.18653/v1/2025.trustnlp-main.35
%U https://aclanthology.org/2025.trustnlp-main.35/
%U https://doi.org/10.18653/v1/2025.trustnlp-main.35
%P 542-551
Markdown (Informal)
[A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models](https://aclanthology.org/2025.trustnlp-main.35/) (Zhang et al., TrustNLP 2025)
ACL