MioFFAn: an Annotation Software for Formula Formalization with LLM Automation Capabilities

Abstract:The automatic translation of mathematical expressions in scientific literature into executable symbolic code (a process we refer to as Formula Formalization) is hindered by a severe scarcity of high-quality, ground-truth datasets specialized for technical scientific domains. In this paper, we present MioFFAn, an open-source, document-centric, and customizable framework designed to facilitate rapid annotation for this task. Building upon the MioGatto architecture, we extend existing features to overcome structural limitations and pivot its scope by introducing specific functionalities for Formula Formalization, such as selection of equations of interest and aided symbolic code specification. By allowing users to configure custom taxonomies and properties for identified symbols, and compatible symbolic operators, we ensure the framework is adaptable to diverse specialized scientific fields. Furthermore, MioFFAn is designed to incorporate partial automation via Large Language Models. By defining a modular set of automated sub-tasks with strict output formats, we enable researchers to iteratively refine automation capabilities and evaluate competing strategies using standard NLP metrics. We specify the current automation methodology and perform a preliminary evaluation that demonstrates to efficacy of this human-in-the-loop approach.

Computation and Language (cs.CL); Machine Learning (cs.LG); Software Engineering (cs.SE)

arXiv:2607.22552 [cs.CL]

(or arXiv:2607.22552v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.22552 Focus to learn more arXiv-issued DOI via DataCite

Proceedings of the 3rd Int. Workshop on Natural Scientific Language Processing (NSLP 2026) at LREC 2026, pages 206-217

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