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Adapting LLMs for Efficient Context Processing through Soft Prompt Compression

Cangqing Wang, Yutian Yang, Ruisi Li, Dan Sun, Ruicong Cai, Yuzhu Zhang, Chengqian Fu, Lillian Floyd

TL;DR

This work tackles the bottleneck of finite context windows and high computational costs in large language models by introducing SPC-LLM, a framework that fuses natural-language summarization with trainable soft prompts to create compact, information-dense representations of long contexts. The method formalizes context compression as $C = f_{LM}(S \oplus f_{S}(f_{NL}(D)))$, combining a NL-prompt generator, a summary-vector compressor, and a trainable soft-prompt layer to optimize downstream tasks. Empirical results demonstrate substantial processing-time reductions (up to $80.1\%$ on SQuAD2.0) alongside improvements in task performance across summarization, sentiment analysis, text classification, and QA, signaling both efficiency and effectiveness gains. The approach suggests a scalable path to extending LLM applicability to real-world, long-document tasks and invites further exploration in multilingual and domain-adaptive settings.

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 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.

Adapting LLMs for Efficient Context Processing through Soft Prompt Compression

TL;DR

This work tackles the bottleneck of finite context windows and high computational costs in large language models by introducing SPC-LLM, a framework that fuses natural-language summarization with trainable soft prompts to create compact, information-dense representations of long contexts. The method formalizes context compression as , combining a NL-prompt generator, a summary-vector compressor, and a trainable soft-prompt layer to optimize downstream tasks. Empirical results demonstrate substantial processing-time reductions (up to on SQuAD2.0) alongside improvements in task performance across summarization, sentiment analysis, text classification, and QA, signaling both efficiency and effectiveness gains. The approach suggests a scalable path to extending LLM applicability to real-world, long-document tasks and invites further exploration in multilingual and domain-adaptive settings.

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 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.
Paper Structure (9 sections, 1 equation, 4 figures, 1 table)

This paper contains 9 sections, 1 equation, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Prior Work
  3. Addressing Lengthy Contexts in LLMs
  4. Soft Prompts in NLP
  5. Text Summarization for Contextual Compression
  6. Methodology
  7. Experiments
  8. Dataset Description
  9. Main Result

Figures (4)

  • Figure 1: An example of successful prompt compression with SPC. The compressed prompt (green) in order to obtain a shorter length and maintain transferability and utility simultaneously than the original long prompt (red).
  • Figure 2: The illustration of SPC shows the compressed conversational answer expect with question.
  • Figure 3: comparison chart of processing times.
  • Figure 4: Performance difference compared to baseline model.