What does this research mean for the field?

The IGA-POLLM framework, utilizing an improved genetic algorithm for prompt optimization, outperforms standard genetic optimization in both accuracy and runtime for extracting structured knowledge from materials literature using large language models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To develop and evaluate IGA-POLLM, a gradient-free genetic algorithm framework designed to optimize prompts for structured information extraction from materials science literature.

September 8, 2026Materials Genome Engineering AdvancesOpen Access

IGA‐POLLM: An Improved Genetic Algorithm for Prompt Optimization in Materials Literature Information Extraction

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To develop and evaluate IGA-POLLM, a gradient-free genetic algorithm framework designed to optimize prompts for structured information extraction from materials science literature.
Formulated prompt engineering as a discrete combinatorial search over six schema-aligned semantic gene components using dynamic crossover/mutation scheduling, tournament selection, and elitist retention.
Evaluated extraction performance across 130 corrosion inhibitor research papers to extract five target fields into strict JSON format using a hierarchical F1-based fitness function with hybrid matching across five publishers and five mainstream large language models.
IGA-POLLM achieved an F1 score of 0.8571 compared to 0.8208 for standard genetic optimization while decreasing execution runtime from 8.2 to 5.6 hours.
The framework maintained a mean F1 score greater than 0.85 across papers from five distinct scientific publishers and transferred robustly across five mainstream LLMs.

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Implication

Computational evaluation demonstrates enhanced information extraction accuracy across materials literature, highlighting effective prompt optimization for automated database curation.

Cite This Study

Xiang et al. (2026) studied this question.

synapsesocial.com/papers/6a9fd82058e84d0ff5b47641 https://doi.org/10.1002/mgea.70097

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Implication

Computational evaluation demonstrates enhanced information extraction accuracy across materials literature, highlighting effective prompt optimization for automated database curation.

IGA‐POLLM: An Improved Genetic Algorithm for Prompt Optimization in Materials Literature Information Extraction

Key Points

Discussion

Implication

Cite This Study

IGA‐POLLM: An Improved Genetic Algorithm for Prompt Optimization in Materials Literature Information Extraction

Key Points

Discussion

Implication

Cite This Study

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