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Method proposed to automatically extract standard experimental preparation procedures from literature with LLM

AI editorPublished 2026.10.29

An approach has been reported that uses a large language model to automatically extract standardized experimental preparation procedures from literature. It can be used to convert materials and chemistry literature into structured procedure data, but general-purpose performance and error rate remain challenges. [1]

1/ AI 활용 방법: learning-free, domain-expandable large language model
2/ 적용 과제: extracting standardized experimental preparation procedure information from literature
3/ 보고된 결과: an approach that automatically extracts standardized preparation procedure was proposed
4/ For literature-based extraction of chemistry and materials information, a large language model (LLM) approach that can be used without training and that can expand across domains was proposed.
5/ This method focuses on automatically extracting preparation procedure described in literature into a standardized format.
6/ From the perspective of materials research, this could structure information critical to reproducibility, such as experimental conditions, reagent use, and process sequence, and connect it to retrieval, comparison, and downstream automation.
7/ In particular, it is directly relevant to literature mining tasks that organize different paper descriptions into the same schema.
8/ However, incomplete literature descriptions, expression differences, and implicit conditions can cause missed extractions or misinterpretations.
9/ In practical research use, automatic extraction results need to be checked against the original text, and post-processing that reflects domain-specific expression differences is necessary. [1]

How to apply this

You can attempt to build a dataset by automatically extracting scattered experimental preparation procedures in literature into standardized items.

  1. First, define a standard schema for procedural items in the paper experimental section.
  2. Use an LLM to extract reagents, concentrations, temperatures, times, and sequence from each paper.
  3. Compare the extraction results item by item with the original sentences and correct errors.
  4. Strengthen rules or dictionaries to reflect domain-specific terminology differences.
Data needed
Text materials such as paper body text, experimental sections, and supplementary information that describe experimental procedures in detail are needed.
Checking the result
Compare automatically extracted conditions with the original text to check the accuracy and omission rate of key items, and compare against manually curated ground truth for sample papers.
What can go wrong
The most common failure modes are incorrectly normalizing papers with different description styles into the same procedure, or the model arbitrarily filling in omitted conditions.

이 가이드는 원문의 방법을 바탕으로 AI가 제안한 적용 방향입니다. 원문에 적힌 내용이 아니며 같은 결과가 재현된다는 보장도 아닙니다.

Related concepts and practice

이 이슈를 읽는 데 필요한 개념입니다. 용어마다 Colab에서 바로 돌려볼 수 있는 실습 노트북이 있습니다.

Sources

  1. A training-free, domain-extensible LLM-based approach for automated extraction of standardized preparation procedures

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