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EMNLP 2025FindingsSingapore-MIT Alliance for ReseaNational University of SingaporeAgency for Science, Technology aSystems, Applications & ProductsAI SingaporeLLM pretraining & scaling laws

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.

Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin and 14 more

Published 2025 · 0 citations

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Agency for Science, Technology aNational University of SingaporeSingapore-MIT Alliance for ReseaChinese University of Hong Kong,LLM pretraining & scaling laws

Position Paper: Data-Centric AI in the Age of Large Language Models

This position paper argues for data-centric AI in the age of large language models and proposes research directions.

Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma and 15 more

Published 2024 · 2 citations

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NeurIPS 2026Nanyang TechnologicalAgency for Science, Technology aA*STARLong-context modeling

Resilient Latent Readouts for Long-Context Question Answering

Jingyi Liao, Wenhao Sun, YITING LI, Zhao Jin and 5 more

Sydney Poster Session 1, Tue, Dec 8, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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NeurIPS 2026Agency for Science, Technology aNanyang TechnologicalI2R, A*STARThe Chinese University of Hong KNational Environment AgencySpatio-temporal forecasting

GeoRad-3D: Factorized Geometry Transport and Residual Radiometry for 3D Radar Nowcasting

YITING LI, Zihan Zhou, Shengkai Chen, Jing Zhang and 6 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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