Uncovering Scaling Laws for Large Language Models via Inverse Problems
Inverse problem methods uncover scaling laws for large language models, revealing predictive relationships between model size, data, and performance from abstract evidence.
Published 2025Paper ↗
57%
OverallWorth a look
?
OverallWorth a lookVote to see the scoreThe exact score shows once you've voted, so every vote is your own call. The first half of each home page shelf shows its scores.
Readers
–
Only vote on papers you've read. Sign in with GitHub to vote.
AI panel1/20reviewers recommend it
lenient 1/5
medium 0/10
strict 0/5
AI panel?Vote to see what the 20 AI reviewers said
Inverting scaling laws is clever, but the inverse framing lacks well-posedness proof and risks overfitting curves rather than recovering true exponents.
Abstract
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, Bryan Kian Hsiang Low. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025.