@inproceedings{verma-etal-2025-uncovering,
title = "Uncovering Scaling Laws for Large Language Models via Inverse Problems",
author = "Verma, Arun and
Wu, Zhaoxuan and
Zhou, Zijian and
Lin, Xiaoqiang and
Chen, Zhiliang and
Sim, Rachael Hwee Ling and
Qiao, Rui and
Wang, Jingtan and
Bui, Nhung and
Niu, Xinyuan and
Hu, Wenyang and
Lau, Gregory Kang Ruey and
Khoo, Zi-Yu and
Zhao, Zitong and
Xu, Xinyi and
Hemachandra, Apivich and
Ng, See-Kiong and
Low, Bryan Kian Hsiang",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-emnlp.1373/",
pages = "25197--25211",
ISBN = "979-8-89176-335-7",
abstract = "Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented complexity and scale in both data and computations. However, due to the high costs of training such models, brute-force trial-and-error approaches to improve LLMs are not feasible. Inspired by the success of inverse problems in uncovering fundamental scientific laws, this position paper advocates that inverse problems can also efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness."
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<abstract>Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented complexity and scale in both data and computations. However, due to the high costs of training such models, brute-force trial-and-error approaches to improve LLMs are not feasible. Inspired by the success of inverse problems in uncovering fundamental scientific laws, this position paper advocates that inverse problems can also efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness.</abstract>
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%0 Conference Proceedings
%T Uncovering Scaling Laws for Large Language Models via Inverse Problems
%A Verma, Arun
%A Wu, Zhaoxuan
%A Zhou, Zijian
%A Lin, Xiaoqiang
%A Chen, Zhiliang
%A Sim, Rachael Hwee Ling
%A Qiao, Rui
%A Wang, Jingtan
%A Bui, Nhung
%A Niu, Xinyuan
%A Hu, Wenyang
%A Lau, Gregory Kang Ruey
%A Khoo, Zi-Yu
%A Zhao, Zitong
%A Xu, Xinyi
%A Hemachandra, Apivich
%A Ng, See-Kiong
%A Low, Bryan Kian Hsiang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F verma-etal-2025-uncovering
%X Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented complexity and scale in both data and computations. However, due to the high costs of training such models, brute-force trial-and-error approaches to improve LLMs are not feasible. Inspired by the success of inverse problems in uncovering fundamental scientific laws, this position paper advocates that inverse problems can also efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness.
%U https://aclanthology.org/2025.findings-emnlp.1373/
%P 25197-25211
Markdown (Informal)
[Uncovering Scaling Laws for Large Language Models via Inverse Problems](https://aclanthology.org/2025.findings-emnlp.1373/) (Verma et al., Findings 2025)
ACL
- Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, and Bryan Kian Hsiang Low. 2025. Uncovering Scaling Laws for Large Language Models via Inverse Problems. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 25197–25211, Suzhou, China. Association for Computational Linguistics.