Natural Language Commanding via Program Synthesis
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Abstract (arXiv)
We present Semantic Interpreter, a natural language-friendly AI system for productivity software such as Microsoft Office that leverages large language models (LLMs) to execute user intent across application features. While LLMs are excellent at understanding user intent expressed as natural language, they are not sufficient for fulfilling application-specific user intent that requires more than text-to-text transformations. We therefore introduce the Office Domain Specific Language (ODSL), a concise, high-level language specialized for performing actions in and interacting with entities in Office applications. Semantic Interpreter leverages an Analysis-Retrieval prompt construction method with LLMs for program synthesis, translating natural language user utterances to ODSL programs that can be transpiled to application APIs and then executed. We focus our discussion primarily on a research exploration for Microsoft PowerPoint.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 34 of its 49 references that are in the map. First posted on arXiv 6 Jun 2023.
Builds on
34 of 49 references.
References · 34
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22 more
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Built on by
Citing papers per year: 2023: 1; 2024: 1; 2025: 2; 2026: 2.
6 papers.
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- 2026PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMscited by 5
- 2025Skill Discovery for Software Scripting Automation via Offline Simulations with LLMscited by 3
- 2024Abductive Symbolic Solver on Abstraction and Reasoning Corpuscited by 2
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- 2026Still Manual? Automated Linter Configuration via DSL-Based LLM Compilation of Coding Standards8 Feb 2026
- 2026PathWise: Planning through World Model for Automated Heuristic Design via Self-Evolving LLMs28 Jan 2026
- 2025Skill Discovery for Software Scripting Automation via Offline Simulations with LLMs29 Apr 2025
- 2025AutoPresent: Designing Structured Visuals from Scratch1 Jan 2025
- 2024Abductive Symbolic Solver on Abstraction and Reasoning Corpus27 Nov 2024
- 2023Large Language Models for Software Engineering: A Systematic Literature Review21 Aug 2023