Evaluating Large Language Models on Controlled Generation Tasks

Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Wieting, Nanyun Peng, Xuezhe Ma


Abstract
While recent studies have looked into the abilities of large language models in various benchmark tasks, including question generation, reading comprehension, multilingual and etc, there have been few studies looking into the controllability of large language models on generation tasks. We present an extensive analysis of various benchmarks including a sentence planning benchmark with different granularities. After comparing large language models against state-of-the-start finetuned smaller models, we present a spectrum showing large language models falling behind, are comparable, or exceed the ability of smaller models. We conclude that *large language models struggle at meeting fine-grained hard constraints*.
Anthology ID:
2023.emnlp-main.190
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3155–3168
Language:
URL:
https://aclanthology.org/2023.emnlp-main.190
DOI:
10.18653/v1/2023.emnlp-main.190
Bibkey:
Cite (ACL):
Jiao Sun, Yufei Tian, Wangchunshu Zhou, Nan Xu, Qian Hu, Rahul Gupta, John Wieting, Nanyun Peng, and Xuezhe Ma. 2023. Evaluating Large Language Models on Controlled Generation Tasks. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3155–3168, Singapore. Association for Computational Linguistics.
Cite (Informal):
Evaluating Large Language Models on Controlled Generation Tasks (Sun et al., EMNLP 2023)
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PDF:
https://aclanthology.org/2023.emnlp-main.190.pdf