![[2412.06106] Efficient Context Propagating Perceiver Architectures for Auto-Regressive Language Modeling](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2412.06106] Efficient Context Propagating Perceiver Architectures for Auto-Regressive Language Modeling
Summary
This article presents the Efficient Context Propagating Perceiver (ECP) architecture, which enhances auto-regressive language modeling by reducing attention complexity while maintaining performance, outperforming existing Transformer models.
Why It Matters
The ECP architecture addresses the critical challenge of high computational costs in Transformer models, making it significant for advancing efficient language modeling techniques. Its ability to maintain performance while reducing complexity could have broad implications for applications in natural language processing and machine learning.
Key Takeaways
- ECP architecture reduces attention complexity to improve efficiency.
- It utilizes both context and latent sequences for better autoregressive training.
- ECP outperforms state-of-the-art Transformer models on multiple benchmarks.
- The architecture maintains the same computational efficiency as LongLoRA.
- Empirical results demonstrate significant improvements in language modeling.
Computer Science > Computation and Language arXiv:2412.06106 (cs) [Submitted on 8 Dec 2024 (v1), last revised 19 Feb 2026 (this version, v2)] Title:Efficient Context Propagating Perceiver Architectures for Auto-Regressive Language Modeling Authors:Kaleel Mahmood, Shaoyi Huang View a PDF of the paper titled Efficient Context Propagating Perceiver Architectures for Auto-Regressive Language Modeling, by Kaleel Mahmood and Shaoyi Huang View PDF HTML (experimental) Abstract:One of the key challenges in Transformer architectures is the quadratic complexity of the attention mechanism, which limits the efficient processing of long sequences. Many recent research works have attempted to provide a reduction from the $O(n^2)$ time complexity of attention to semi-linear complexity. However, it remains an unsolved problem in the sense of maintaining high performance when complexity is reduced. One of the important works in this respect is the Perceiver class of architectures that have demonstrated excellent performance, while reducing the computation complexity. In this paper, we use the PerceiverAR as a basis and explore the design space of different trade-offs between preserving context and reducing attention complexity. To this end, we develop four new architectural paradigms, the best performing of which we denote as the Efficient Context propagating Perceiver (ECP). ECP has two major advantages over the PerceiverAR. First, the ECP architecture overcomes the main drawback of Percie...