LLM-based variable extraction outperforms rule-based methods on a new 22-paper benchmark, with best F1 around 0.53 to 0.64, but all systems remain far from solved.
AutoMATES: Automated Model Assembly from Text, Equations, and Software
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abstract
Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations. But to be of real use, they must also be implemented as software, thus making code a third form of representing models. We introduce the AutoMATES project, which aims to build semantically-rich unified representations of models from scientific code and publications to facilitate the integration of computational models from different domains and allow for modeling large, complicated systems that span multiple domains and levels of abstraction.
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Variable Extraction for Model Recovery in Scientific Literature
LLM-based variable extraction outperforms rule-based methods on a new 22-paper benchmark, with best F1 around 0.53 to 0.64, but all systems remain far from solved.