Advancing Natural Language Processing: Harnessing Language Model Programming and Query Language for Efficient and Ethical AI

Abstract: The rapid advancement of large language models (LLMs) has revolutionized the field of natural language processing (NLP), enabling significant improvements in tasks such as question answering, code generation, and text completion. However, the effective utilization of LLMs often requires complex, task-specific programs and ad-hoc interactions, leading to inefficiencies and increased computational costs. This paper introduces Language Model Programming (LMP) and the Language Model Query Language (LMQL) as novel frameworks that generalize prompting by integrating scripting and constraints. LMP provides a high-level abstraction that simplifies the adaptation of LLMs to diverse tasks, while LMQL optimizes inference procedures, reducing computational overhead and achieving significant cost savings. Through a comprehensive literature review, we explore the evolution of LLM prompting techniques, the limitations of existing methods, and the transformative potential of LMP and LMQL. Our findings demonstrate that these frameworks not only enhance the efficiency and accuracy of LLMs but also democratize access to advanced AI capabilities, ensuring their responsible and ethical use. The implications of LMP and LMQL extend beyond technical advancements, offering a pathway toward more accessible, powerful, and ethically aligned AI systems.

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Keywords: Large Language Models (LLMs), Natural Language Processing (NLP), Language Model Programming (LMP), Language Model Query Language (LMQL), Prompting Techniques, Scripting and Constraints, Computational Efficiency, AI Ethics, Zero-Shot Learning, Few-Shot Learning, Constrained Decoding, Interactive AI Systems, Cost Optimization, AI Democratization.