# Adapting LLMs for Efficient Context Processing through Soft Prompt Compression arXiv: https://arxiv.org/abs/2404.04997 ยท HTML: https://mlsanity.com/paper/2404.04997/ ## Key facts - Cited by 22 papers in the map (data through 14 Oct 2026). - Placed in Prompt and context compression, one of 12 directions in Efficient LLM inference and serving. - Builds on 9 of its 14 references that are in the map. - First posted on arXiv 7 Apr 2024. ## Placed in - Efficient LLM inference and serving / [Prompt and context compression](https://mlsanity.com/inference/prompt-and-context-compression/) ## Builds on (most cited first, 9 of 9) - [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019) - [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021) - [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021) - [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020) - [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020) - [TaskBench: Benchmarking Large Language Models for Task Automation](https://mlsanity.com/paper/2311.18760/) (2023) - [The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts](https://mlsanity.com/paper/2401.13136/) (2024) - [On Transferability of Prompt Tuning for Natural Language Processing](https://mlsanity.com/paper/2111.06719/) (2021) - [Enhancing Multi-Hop Knowledge Graph Reasoning through Reward Shaping Techniques](https://mlsanity.com/paper/2403.05801/) (2024) ## Built on by (most cited first, 22 of 22) - [Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning](https://mlsanity.com/paper/2508.19828/) (2025) - [Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM Inference](https://mlsanity.com/paper/2409.01227/) (2024) - [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024) - [LongCodeZip: Compress Long Context for Code Language Models](https://mlsanity.com/paper/2510.00446/) (2025) - [A Survey of Token Compression for Efficient Multimodal Large Language Models](https://mlsanity.com/paper/2507.20198/) (2025) - [Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees](https://mlsanity.com/paper/2405.13290/) (2024) - [Towards General Continuous Memory for Vision-Language Models](https://mlsanity.com/paper/2505.17670/) (2025) - [Abductive Inference in Retrieval-Augmented Language Models: Generating and Validating Missing Premises](https://mlsanity.com/paper/2511.04020/) (2025) - [Prompt Compression in the Wild: Measuring Latency, Rate Adherence, and Quality for Faster LLM Inference](https://mlsanity.com/paper/2604.02985/) (2026) - [Hybrid Fuzzing with LLM-Guided Input Mutation and Semantic Feedback](https://mlsanity.com/paper/2511.03995/) (2025) - [Chain-of-Memory: Lightweight Memory Construction with Dynamic Evolution for LLM Agents](https://mlsanity.com/paper/2601.14287/) (2026) - [Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs](https://mlsanity.com/paper/2404.12587/) (2024) - [PIS: Linking Importance Sampling and Attention Mechanisms for Efficient Prompt Compression](https://mlsanity.com/paper/2504.16574/) (2025) - [CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction](https://mlsanity.com/paper/2409.13701/) (2024) - [Advancements in Natural Language Processing: Exploring Transformer-Based Architectures for Text Understanding](https://mlsanity.com/paper/2503.20227/) (2025) - [Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition](https://mlsanity.com/paper/2504.20946/) (2025) - [From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap](https://mlsanity.com/paper/2410.20791/) (2024) - [Context-Adaptive Synthesis and Compression for Enhanced Retrieval-Augmented Generation in Complex Domains](https://mlsanity.com/paper/2508.19357/) (2025) - [HarmTransform: Transforming Explicit Harmful Queries into Stealthy via Multi-Agent Debate](https://mlsanity.com/paper/2512.23717/) (2025) - [Task-agnostic Prompt Compression with Context-aware Sentence Embedding and Reward-guided Task Descriptor](https://mlsanity.com/paper/2502.13374/) (2025) - [Mockingbird: How does LLM perform in general machine learning tasks?](https://mlsanity.com/paper/2508.04279/) (2025) - [JSPLIT: A Taxonomy-based Solution for Prompt Bloating in Model Context Protocol](https://mlsanity.com/paper/2510.14537/) (2025) ## Abstract (arXiv) 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.