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1.0 The Paradigm Shift: From Recurrence to Parallel Self-Attention Prior to 2017, the field of sequence modeling and transduction was dominated by complex recurrent neural networks (RNNs), specifically Long Short-Term Read More …
Part 1: The Rationale for Parameter-Efficient Adaptation 1.1. The Adaptation Imperative: The “Fine-Tuning Crisis” The modern paradigm of natural language processing is built upon a two-stage process: large-scale, general-domain pre-training Read More …
Executive Summary The landscape of large language models (LLMs) is currently defined by an intense competitive escalation, often termed the “Context Window Arms Race.” This trend, marked by the exponential Read More …
1. Executive Summary 1.1. Strategic Synopsis The advent of multimodal foundation models (FMs) represents a profound paradigm shift in artificial intelligence, moving beyond the capabilities of single-modality systems to enable Read More …