Large language model-enabled automated data extraction for concrete materials informatics

arXiv:2604.22938 · cond-mat.mtrl-sci, cs.CL, cs.LG · Submitted 2026-04-24 · Read on arXiv

cond-mat.mtrl-sci, cs.CL, cs.LG

Submitted: 2026-04-24

Updated: 2026-08-31

Comments: 46 pages, including Supplementary Information; accepted for publication in npj Computational Materials

Code: https://github.com/deepseek-ai/DeepSeek-R1

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• F1 Score — 14× in this paper · explained in 3 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Generalization — 9× in this paper · explained in 13 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Foundation Model — 4× in this paper · explained in 3 episodes
• Benchmarking — 3× in this paper · explained in 3 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Adaptability — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Prompt Engineering — 2× in this paper · explained in 4 episodes
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Contextual Understanding — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Modular Architecture — 1× in this paper · explained in 4 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Two-dimensional Uniform Manifold Approximation and Projection — 1× in this paper

Sources

• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Scaling Laws for Neural Language Models
• Deep Learning Scaling is Predictable, Empirically
• Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges
• Leveraging Vision Capabilities of Multimodal LLMs for Automated Data Extraction from Plots
• ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning

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