# Flexible, Model-Agnostic Method for Materials Data Extraction from Text Using General Purpose Language Models

arXiv: https://arxiv.org/abs/2302.04914 · HTML: https://mlsanity.com/paper/2302.04914/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 65 references that are in the map.
- First posted on arXiv 9 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [MatSciBERT: A Materials Domain Language Model for Text Mining and Information Extraction](https://mlsanity.com/paper/2109.15290/) (2021)
- [Structured information extraction from complex scientific text with fine-tuned large language models](https://mlsanity.com/paper/2212.05238/) (2022)
- [Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks](https://mlsanity.com/paper/1901.00032/) (2018)

## Built on by (most cited first, 9 of 9)

- [Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering](https://mlsanity.com/paper/2303.05352/) (2023)
- [Toward Reliable Ad-hoc Scientific Information Extraction: A Case Study on Two Materials Datasets](https://mlsanity.com/paper/2406.05348/) (2024)
- [Enabling and Analyzing How to Efficiently Extract Information from Hybrid Long Documents with LLMs](https://mlsanity.com/paper/2305.16344/) (2023)
- [ComProScanner: A multi-agent based framework for composition-property structured data extraction from scientific literature](https://mlsanity.com/paper/2510.20362/) (2025)
- [GPT-4 as an interface between researchers and computational software: improving usability and reproducibility](https://mlsanity.com/paper/2310.11458/) (2023)
- [Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset](https://mlsanity.com/paper/2412.20072/) (2024)
- [Towards Large Language Models for Lunar Mission Planning and In Situ Resource Utilization](https://mlsanity.com/paper/2504.20125/) (2025)
- [Using a Human-AI Teaming Approach to Create and Curate Scientific Datasets with the SCILIRE System](https://mlsanity.com/paper/2603.12638/) (2026)
- [Large language model-enabled automated data extraction for concrete materials informatics](https://mlsanity.com/paper/2604.22938/) (2026)

## Abstract (arXiv)

Accurate and comprehensive material databases extracted from research papers are crucial for materials science and engineering, but their development requires significant human effort. With large language models (LLMs) transforming the way humans interact with text, LLMs provide an opportunity to revolutionize data extraction. In this study, we demonstrate a simple and efficient method for extracting materials data from full-text research papers leveraging the capabilities of LLMs combined with human supervision. This approach is particularly suitable for mid-sized databases and requires minimal to no coding or prior knowledge about the extracted property. It offers high recall and nearly perfect precision in the resulting database. The method is easily adaptable to new and superior language models, ensuring continued utility. We show this by evaluating and comparing its performance on GPT-3 and GPT-3.5/4 (which underlie ChatGPT), as well as free alternatives such as BART and DeBERTaV3. We provide a detailed analysis of the method's performance in extracting sentences containing bulk modulus data, achieving up to 90% precision at 96% recall, depending on the amount of human effort involved. We further demonstrate the method's broader effectiveness by developing a database of critical cooling rates for metallic glasses over twice the size of previous human curated databases.