Meaning-Typed Programming: Language-level Abstractions and Runtime for GenAI Applications

• URL: http://arxiv.org/abs/2405.08965v3
• Date: Thu, 16 Jan 2025 18:56:27 GMT
• Title: Meaning-Typed Programming: Language-level Abstractions and Runtime for GenAI Applications
• Authors: Jason Mars, Yiping Kang, Jayanaka L. Dantanarayana, Kugesan Sivasothynathan, Christopher Clarke, Baichuan Li, Krisztian Flautner, Lingjia Tang,
• Abstract summary: Software is rapidly evolving from logical code to neuro-integrated applications that leverage generative AI and large language models (LLMs) for application functionality.<n>This paper proposes meaning-typed programming (MTP), a novel approach to simplify the creation of neuro-integrated applications.
• Score: 8.308424118055981
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Software is rapidly evolving from being programmed with traditional logical code, to neuro-integrated applications that leverage generative AI and large language models (LLMs) for application functionality. This shift increases the complexity of building applications, as developers now must reasoning about, program, and prompt LLMs. Despite efforts to create tools to assist with prompt engineering, these solutions often introduce additional layers of complexity to the development of neuro-integrated applications. This paper proposes meaning-typed programming (MTP), a novel approach to simplify the creation of neuro-integrated applications by introducing new language-level abstractions that hide the complexities of LLM integration. Our key insight is that typical conventional code already possesses a high level of semantic richness that can be automatically reasoned about, as it is designed to be readable and maintainable by humans. Leveraging this insight, we conceptualize LLMs as meaning-typed code constructs and introduce a by abstraction at the language level, MT-IR, a new meaning-based intermediate representation at the compiler level, and MT Runtime, an automated run-time engine for LLM integration and operations. We implement MTP in a production-grade Python super-set language called Jac and perform an extensive evaluation. Our results demonstrate that MTP not only simplifies the development process but also meets or exceeds the efficacy of state-of-the-art manual and tool-assisted prompt engineering techniques in terms of accuracy and usability.

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