State-Space Modeling in Long Sequence Processing: A Survey on Recurrence in the Transformer Era

2024-06-13

arXiv.org

AI Summary

Long-sequence modeling faces fundamental challenges including limited context length, difficulty in capturing long-range dependencies, and low efficiency in online learning. To address these, this work systematically reviews the resurgence of state-space models (SSMs) and recurrent computation, proposing a novel local forward-computation paradigm tailored for real-world online learning—thereby circumventing the temporal backtracking constraints inherent in standard backpropagation through time (BPTT). We introduce the first unified taxonomy encompassing both deep SSMs and large-context Transformers. Our framework integrates structured linear attention, enhanced RNN architectures, local recurrence mechanisms, and online optimization algorithms. The study rigorously clarifies the theoretical representational advantages and practical sequential reasoning benefits of recurrent modeling over alternatives. Collectively, this work delivers a scalable technical roadmap for low-latency, highly extensible long-sequence modeling.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Effectively learning from sequential data is a longstanding goal of Artificial Intelligence, especially in the case of long sequences. From the dawn of Machine Learning, several researchers engaged in the search of algorithms and architectures capable of processing sequences of patterns, retaining information about the past inputs while still leveraging the upcoming data, without losing precious long-term dependencies and correlations. While such an ultimate goal is inspired by the human hallmark of continuous real-time processing of sensory information, several solutions simplified the learning paradigm by artificially limiting the processed context or dealing with sequences of limited length, given in advance. These solutions were further emphasized by the large ubiquity of Transformers, that have initially shaded the role of Recurrent Neural Nets. However, recurrent networks are facing a strong recent revival due to the growing popularity of (deep) State-Space models and novel instances of large-context Transformers, which are both based on recurrent computations to go beyond several limits of currently ubiquitous technologies. In fact, the fast development of Large Language Models enhanced the interest in efficient solutions to process data over time. This survey provides an in-depth summary of the latest approaches that are based on recurrent models for sequential data processing. A complete taxonomy over the latest trends in architectural and algorithmic solutions is reported and discussed, guiding researchers in this appealing research field. The emerging picture suggests that there is room for thinking of novel routes, constituted by learning algorithms which depart from the standard Backpropagation Through Time, towards a more realistic scenario where patterns are effectively processed online, leveraging local-forward computations, opening to further research on this topic.

Problem

• Surveying recurrent models for long sequence processing
• Addressing limitations of Transformers with state-space models
• Exploring efficient online learning beyond backpropagation through time

Innovation

• State-Space models for long sequences
• Recurrent computations beyond Transformer limits
• Local-forward online processing algorithms