Bridging Locality and Globality: Hierarchical Transformer Architectures for Infinite Context
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| Format: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901622325510144 |
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| author | Revista, Zen IA, 10 |
| author_facet | Revista, Zen IA, 10 |
| contents | Transformer architectures have revolutionized natural language processing and various other domains due to their ability to capture long-range dependencies through self-attention mechanisms. However, the quadratic complexity of standard self-attention with respect to sequence length imposes a fundamental limitation on the context window size, restricting their application to tasks involving very long documents or data streams. This paper proposes and rigorously investigates Hierarchical Transformer Architectures designed to bridge the gap between local and global information processing, thereby enabling effective handling of theoretically infinite contexts. We introduce a multi-layered attention mechanism where lower layers focus on fine-grained local interactions within fixed-size segments, and higher layers attend to aggregated representations of these segments, capturing broader contextual relationships. This approach mitigates the computational burden while preserving critical information across extensive sequences. Our methodology outlines a specific architecture incorporating segment-level attention, hierarchical pooling, and global attention over compressed representations. We present a comprehensive literature review of existing methods for long-context Transformers and discuss the theoretical underpinnings of our proposed model. Hypothetical results demonstrate significant improvements in computational efficiency, memory footprint, and performance on long-sequence tasks such as document summarization, question answering over extensive texts, and large-scale code generation. The discussion highlights the architectural flexibility, potential for transfer learning, and challenges related to optimal segment sizing and hierarchical depth. We conclude that hierarchical Transformers offer a robust and scalable solution for extending the contextual capabilities of Transformer models, opening new avenues for processing unbounded sequential data. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17798508 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Bridging Locality and Globality: Hierarchical Transformer Architectures for Infinite Context Revista, Zen IA, 10 Transformer architectures have revolutionized natural language processing and various other domains due to their ability to capture long-range dependencies through self-attention mechanisms. However, the quadratic complexity of standard self-attention with respect to sequence length imposes a fundamental limitation on the context window size, restricting their application to tasks involving very long documents or data streams. This paper proposes and rigorously investigates Hierarchical Transformer Architectures designed to bridge the gap between local and global information processing, thereby enabling effective handling of theoretically infinite contexts. We introduce a multi-layered attention mechanism where lower layers focus on fine-grained local interactions within fixed-size segments, and higher layers attend to aggregated representations of these segments, capturing broader contextual relationships. This approach mitigates the computational burden while preserving critical information across extensive sequences. Our methodology outlines a specific architecture incorporating segment-level attention, hierarchical pooling, and global attention over compressed representations. We present a comprehensive literature review of existing methods for long-context Transformers and discuss the theoretical underpinnings of our proposed model. Hypothetical results demonstrate significant improvements in computational efficiency, memory footprint, and performance on long-sequence tasks such as document summarization, question answering over extensive texts, and large-scale code generation. The discussion highlights the architectural flexibility, potential for transfer learning, and challenges related to optimal segment sizing and hierarchical depth. We conclude that hierarchical Transformers offer a robust and scalable solution for extending the contextual capabilities of Transformer models, opening new avenues for processing unbounded sequential data. |
| title | Bridging Locality and Globality: Hierarchical Transformer Architectures for Infinite Context |
| url | https://doi.org/10.5281/zenodo.17798508 |