From Static Inference to Dynamic Interaction: Navigating the Landscape of Streaming Large Language Models
arXiv:2603.04592v1 Announce Type: new Abstract: Standard Large Language Models (LLMs) are predominantly designed for static inference with pre-defined inputs, which limits their applicability in dynamic, real-time scenarios. To address this gap, the streaming LLM paradigm has emerged. However, existing definitions of streaming LLMs remain fragmented, conflating streaming generation, streaming inputs, and interactive streaming architectures, while a systematic taxonomy is still lacking. This paper provides a comprehensive overview and analysis of streaming LLMs. First, we establish a unified definition of streaming LLMs based on data flow and dynamic interaction to clarify existing ambiguities. Building on this definition, we propose a systematic taxonomy of current streaming LLMs and conduct an in-depth discussion on their underlying methodologies. Furthermore, we explore the applications of streaming LLMs in real-world scenarios and outline promising research directions to support on
arXiv:2603.04592v1 Announce Type: new Abstract: Standard Large Language Models (LLMs) are predominantly designed for static inference with pre-defined inputs, which limits their applicability in dynamic, real-time scenarios. To address this gap, the streaming LLM paradigm has emerged. However, existing definitions of streaming LLMs remain fragmented, conflating streaming generation, streaming inputs, and interactive streaming architectures, while a systematic taxonomy is still lacking. This paper provides a comprehensive overview and analysis of streaming LLMs. First, we establish a unified definition of streaming LLMs based on data flow and dynamic interaction to clarify existing ambiguities. Building on this definition, we propose a systematic taxonomy of current streaming LLMs and conduct an in-depth discussion on their underlying methodologies. Furthermore, we explore the applications of streaming LLMs in real-world scenarios and outline promising research directions to support ongoing advances in streaming intelligence. We maintain a continuously updated repository of relevant papers at https://github.com/EIT-NLP/Awesome-Streaming-LLMs.
Executive Summary
This article presents a comprehensive analysis of the streaming Large Language Models (LLMs) paradigm, addressing existing ambiguities and proposing a systematic taxonomy. The authors establish a unified definition of streaming LLMs based on data flow and dynamic interaction, and conduct an in-depth discussion on their underlying methodologies. The paper explores applications of streaming LLMs in real-world scenarios and outlines promising research directions. A continuously updated repository of relevant papers is maintained. This article contributes significantly to the field of streaming intelligence, providing a much-needed framework for understanding and advancing this area. Its implications are far-reaching, with potential applications in natural language processing, artificial intelligence, and beyond.
Key Points
- ▸ Establishes a unified definition of streaming LLMs based on data flow and dynamic interaction
- ▸ Proposes a systematic taxonomy of current streaming LLMs
- ▸ Discusses underlying methodologies and applications in real-world scenarios
Merits
Comprehensive Analysis
The article provides a thorough examination of the streaming LLMs paradigm, addressing existing ambiguities and proposing a systematic taxonomy.
Unified Definition
The authors establish a unified definition of streaming LLMs, clarifying existing ambiguities and providing a clear understanding of the paradigm.
Research Directions
The paper outlines promising research directions to support ongoing advances in streaming intelligence, contributing to the development of this area.
Demerits
Limited Scope
The article focuses primarily on the streaming LLMs paradigm, potentially overlooking other relevant areas in natural language processing and artificial intelligence.
Complexity
The taxonomy and underlying methodologies proposed in the article may be complex, requiring significant expertise to fully understand and apply.
Expert Commentary
This article marks a significant milestone in the development of streaming LLMs, providing a much-needed framework for understanding and advancing this area. The authors' comprehensive analysis and proposed taxonomy will undoubtedly contribute to the growth of this field, with far-reaching implications for natural language processing, artificial intelligence, and beyond. While the article's focus on streaming LLMs may limit its scope, its significance lies in its ability to clarify existing ambiguities and provide a clear direction for future research. As the field of streaming intelligence continues to evolve, this article will serve as a crucial reference point, informing the development of more sophisticated and effective models.
Recommendations
- ✓ Further research should focus on developing more efficient and scalable streaming LLMs, addressing the complexity and computational requirements associated with this paradigm.
- ✓ The development of standards and guidelines for the deployment and use of streaming LLMs in real-world scenarios is essential to ensure their safe and effective adoption.