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 the
synergies among natural language summarization, soft prompt compression, and
augmented utility preservation mechanisms. Our methodology, dubbed
SoftPromptComp, amalgamates natural language prompts extracted from
summarization methodologies with dynamically generated soft prompts to forge a
concise yet semantically robust depiction of protracted contexts. This
depiction undergoes further refinement via a weighting mechanism optimizing
information retention and utility for subsequent tasks. We substantiate that
our framework markedly diminishes computational overhead and enhances LLMs'
efficacy across various benchmarks, while upholding or even augmenting the
caliber of the produced content. By amalgamating soft prompt compression with
sophisticated summarization, SoftPromptComp confronts the dual challenges of
managing lengthy contexts and ensuring model scalability. Our findings point
towards a propitious trajectory for augmenting LLMs' applicability and
efficiency, rendering them more versatile and pragmatic for real-world
applications. This research enriches the ongoing discourse on optimizing
language models, providing insights into the potency of soft prompts and
summarization techniques as pivotal instruments for the forthcoming generation
of NLP solutions.

该研究提出了一种名为 SoftPromptComp 的创新框架，它通过集成自然语言摘要、软提示压缩和增强型效用保留机制来为大语言模型提供流畅的上下文处理。研究结果表明，该框架显著降低了计算负担，并提高了大语言模型在各项基准测试中的效力，同时保持或增强所生成内容的质量。该研究为优化语言模型提供了见解，并探讨了软提示和摘要技术作为未来自然语言处理解决方案的关键工具的潜力。

通过软提示压缩使 LLMs 适应高效上下文处理

Adapting LLMs for Efficient Context Processing through Soft Prompt  Compression

With an increased interest in the production of personal health technologies
designed to track user data (e.g., nutrient intake, step counts), there is now
more opportunity than ever to surface meaningful behavioral insights to
everyday users in the form of natural language. This knowledge can increase
their behavioral awareness and allow them to take action to meet their health
goals. It can also bridge the gap between the vast collection of personal
health data and the summary generation required to describe an individual's
behavioral tendencies. Previous work has focused on rule-based time-series data
summarization methods designed to generate natural language summaries of
interesting patterns found within temporal personal health data. We examine
recurrent, convolutional, and Transformer-based encoder-decoder models to
automatically generate natural language summaries from numeric temporal
personal health data. We showcase the effectiveness of our models on real user
health data logged in MyFitnessPal and show that we can automatically generate
high-quality natural language summaries. Our work serves as a first step
towards the ambitious goal of automatically generating novel and meaningful
temporal summaries from personal health data.

本文旨在研究如何利用编码 - 解码模型，从个人健康数据中自动生成高质量的自然语言总结，在帮助用户了解自己的健康状况和行为习惯的基础上，摆脱个人健康数据量大、上下文不连贯等问题，实现个人健康数据自动化总结的初步目标。