Adapting LLMs for Efficient Context Processing through Soft Prompt Compression

arXiv:2404.04997v1 Announce Type: new
Abstract: The rapid advancement of Large Language Models (LLMs) has inaugurated a transformative epoch in natural language processing, fostering unprecedented proficiency in text generation, comprehension, and contextual scrutiny. Nevertheless, effectively handling extensive contexts, crucial for myriad applications, poses a formidable obstacle owing to the intrinsic constraints of the models' context window sizes and the computational burdens entailed by their operations. This investigation presents an innovative framework that strategically tailors LLMs for streamlined context processing by harnessing …

abstract advancement applications arxiv compression constraints context cs.ai cs.cl cs.lg intrinsic language language models language processing large language large language models llms natural natural language natural language processing processing prompt text text generation through type