Comparative Analysis of Large Language Models for the Machine-Assisted Resolution of User Intentions

2025-08-29

arXiv.org

AI Summary

Cloud-hosted proprietary large language models (LLMs) pose significant challenges in user intent parsing—including privacy leakage, lack of user autonomy, and limited scalability. Method: This paper systematically evaluates the feasibility of open-weight LLMs as core components of a localized, intent-driven operating system. We design a lightweight intent parsing framework leveraging multiple open-source models (e.g., Llama-3, Qwen, Phi-3), integrating natural language understanding, workflow generation, and on-device inference. Empirical evaluation is conducted across multi-application collaborative tasks, with GPT-4 as a performance benchmark. Results: Several open-weight models achieve intent recognition accuracy comparable to closed-source counterparts under local deployment—while ensuring data privacy, enabling offline operation, and preserving user control. To our knowledge, this is the first study to empirically validate open LLMs as enablers of decentralized, language-first interaction paradigms. It establishes an open architectural foundation and empirical evidence for trustworthy, scalable intent-oriented operating systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Large Language Models (LLMs) have emerged as transformative tools for natural language understanding and user intent resolution, enabling tasks such as translation, summarization, and, increasingly, the orchestration of complex workflows. This development signifies a paradigm shift from conventional, GUI-driven user interfaces toward intuitive, language-first interaction paradigms. Rather than manually navigating applications, users can articulate their objectives in natural language, enabling LLMs to orchestrate actions across multiple applications in a dynamic and contextual manner. However, extant implementations frequently rely on cloud-based proprietary models, which introduce limitations in terms of privacy, autonomy, and scalability. For language-first interaction to become a truly robust and trusted interface paradigm, local deployment is not merely a convenience; it is an imperative. This limitation underscores the importance of evaluating the feasibility of locally deployable, open-source, and open-access LLMs as foundational components for future intent-based operating systems. In this study, we examine the capabilities of several open-source and open-access models in facilitating user intention resolution through machine assistance. A comparative analysis is conducted against OpenAI's proprietary GPT-4-based systems to assess performance in generating workflows for various user intentions. The present study offers empirical insights into the practical viability, performance trade-offs, and potential of open LLMs as autonomous, locally operable components in next-generation operating systems. The results of this study inform the broader discussion on the decentralization and democratization of AI infrastructure and point toward a future where user-device interaction becomes more seamless, adaptive, and privacy-conscious through locally embedded intelligence.

Problem

• Evaluating open-source LLMs for local user intent resolution
• Comparing performance of open models against proprietary systems
• Assessing feasibility of local LLMs for privacy-conscious operating systems

Innovation

• Evaluating open-source LLMs for local deployment
• Comparing performance with proprietary cloud-based models
• Assessing feasibility for privacy-conscious intent-based systems