Background
Modern deep learning models, particularly transformer-based architectures like BERT, GPT, and their variants, have achieved state-of-the-art performance across a wide range of natural language processing (NLP) tasks. However, these models are typically static in nature, meaning they use the same computational pathway and resources for all inputs, regardless of complexity. This leads to several inefficiencies:
Over-Computation for Simple Inputs: Easier inputs (e.g., short sentences, straightforward queries) do not require the full capacity of the model, yet they are processed using the same number of layers and parameters as harder inputs.Under-Computation for Complex Inputs: Harder inputs (e.g., long documents, ambiguous queries) may require more computational resources than the model can provide, leading to suboptimal performance.Resource Inefficiency: Static models consume the same amount of computational resources (e.g., memory, energy, time) for all inputs, which is wasteful and limits scalability, especially for real-time or resource-constrained applications.Problem Statement
How can we design dynamic neural architectures that adapt their size, complexity, and computational pathways based on:
Input Complexity: Easier inputs should require less computation, while harder inputs should trigger more complex processing.Task Requirements: Different tasks (e.g., classification, summarization, question answering) may require different levels of model capacity or specialization.Resource Constraints: The model should be able to adjust its computation based on available resources (e.g., CPU, GPU, memory) to ensure efficient deployment in diverse environments.The goal is to create models that are efficient, scalable, and adaptive, without sacrificing performance on downstream tasks.
