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

2024-05-14

AI Summary

To address the complexity of LLM integration and the heavy engineering burden of prompt design in generative AI application development, this paper proposes Meaning-Typed Programming (MTP), a novel programming paradigm. MTP treats large language models as semantically interpretable, native code constructs via language-level abstractions and a dedicated runtime system, thereby eliminating explicit prompt authoring. Its core contributions include: (i) the first meaning-oriented intermediate representation (MT-IR); (ii) a compiler-level automatic semantic understanding mechanism; (iii) a dynamic LLM orchestration engine for adaptive model coordination; and (iv) Jac—a Python superset implementation framework. Experimental evaluation demonstrates that MTP achieves accuracy and developer productivity on par with or exceeding state-of-the-art manual and tool-assisted prompt engineering approaches, while substantially reducing development complexity for neuro-symbolic applications.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Artificial Intelligence
• Large Language Models
• Software Development

Innovation

• Meaning Type Programming
• Artificial Intelligence
• Large Language Models