# Multi-modal Synthesis of Regular Expressions

arXiv: https://arxiv.org/abs/1908.03316 · HTML: https://mlsanity.com/paper/1908.03316/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2019.

## Built on by (most cited first, 13 of 13)

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- [Data Formulator 2: Iterative Creation of Data Visualizations, with AI Transforming Data Along the Way](https://mlsanity.com/paper/2408.16119/) (2024)
- [Multi-modal Program Inference: a Marriage of Pre-trainedLanguage Models and Component-based Synthesis](https://mlsanity.com/paper/2109.02445/) (2021)
- [InfeRE: Step-by-Step Regex Generation via Chain of Inference](https://mlsanity.com/paper/2308.04041/) (2023)
- [ImageEye: Batch Image Processing Using Program Synthesis](https://mlsanity.com/paper/2304.03253/) (2023)
- [Efficient Bottom-Up Synthesis for Programs with Local Variables](https://mlsanity.com/paper/2311.03705/) (2023)
- [Making Table Understanding Work in Practice](https://mlsanity.com/paper/2109.05173/) (2021)
- [Gensors: Authoring Personalized Visual Sensors with Multimodal Foundation Models and Reasoning](https://mlsanity.com/paper/2501.15727/) (2025)
- [On Synthesis of Timed Regular Expressions](https://mlsanity.com/paper/2509.06262/) (2025)
- [Regular Expressions for Fast-response COVID-19 Text Classification](https://mlsanity.com/paper/2102.09507/) (2021)
- [Neuro-Symbolic Regex Synthesis Framework via Neural Example Splitting](https://mlsanity.com/paper/2205.11258/) (2022)
- [RuleChef: Grounding LLM Task Knowledge in Human-Editable Rules](https://mlsanity.com/paper/2607.01293/) (2026)

## Abstract (arXiv)

In this paper, we propose a multi-modal synthesis technique for automatically constructing regular expressions (regexes) from a combination of examples and natural language. Using multiple modalities is useful in this context because natural language alone is often highly ambiguous, whereas examples in isolation are often not sufficient for conveying user intent. Our proposed technique first parses the English description into a so-called hierarchical sketch that guides our programming-by-example (PBE) engine. Since the hierarchical sketch captures crucial hints, the PBE engine can leverage this information to both prioritize the search as well as make useful deductions for pruning the search space. We have implemented the proposed technique in a tool called Regel and evaluate it on over three hundred regexes. Our evaluation shows that Regel achieves 80% accuracy whereas the NLP-only and PBE-only baselines achieve 43% and 26% respectively. We also compare our proposed PBE engine against an adaptation of AlphaRegex, a state-of-the-art regex synthesis tool, and show that our proposed PBE engine is an order of magnitude faster, even if we adapt the search algorithm of AlphaRegex to leverage the sketch. Finally, we conduct a user study involving 20 participants and show that users are twice as likely to successfully come up with the desired regex using Regel compared to without it.