Large Language Models (LLMs) are revolutionizing natural language processing, but their reliance on attention mechanisms in Transformer frameworks leads to impractical computing complexity for processing large text sequences. To address this, substitutes like State Space Models and the Based model have been proposed. Tinkoff researchers introduced ReBased, an improved version, to enhance the attention process and achieve superior performance in tasks involving long sequences. The study highlights the potential of kernel-based methods and the need for further exploration to optimize performance in natural language processing tasks.
New Standards in Natural Language Processing
Revolutionizing Large Language Models
Large Language Models (LLMs) are setting new standards across various activities, revolutionizing natural language processing. However, their reliance on attention mechanisms in Transformer frameworks has caused impractical computing complexity, especially with large text sequences.
Practical Solutions and Value
Several substitutes for Transformers have been proposed to address these limitations. State Space Models (SSMs) and Linear Transformers offer alternative methods with improved text modeling quality and better management of long-term text dependencies.
Refining the kernel function and introducing new architectural improvements, Tinkoff researchers have developed “ReBased,” an improved variant of the Linear Transformer model. ReBased outperforms its predecessor in various scenarios and has shown promising results in handling tasks involving extensive copying or remembering past context.
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