# Style-Compress: An LLM-Based Prompt Compression Framework Considering Task-Specific Styles

arXiv: https://arxiv.org/abs/2410.14042 · HTML: https://mlsanity.com/paper/2410.14042/

## Key facts

- Cited by 9 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 13 of its 22 references that are in the map.
- First posted on arXiv 17 Oct 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, 13 of 13)

- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond](https://mlsanity.com/paper/2304.13712/) (2023)
- [Adapting Language Models to Compress Contexts](https://mlsanity.com/paper/2305.14788/) (2023)
- [RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation](https://mlsanity.com/paper/2310.04408/) (2023)
- [Compressing Context to Enhance Inference Efficiency of Large Language Models](https://mlsanity.com/paper/2310.06201/) (2023)
- [Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models](https://mlsanity.com/paper/2210.03162/) (2022)
- [Discrete Prompt Compression with Reinforcement Learning](https://mlsanity.com/paper/2308.08758/) (2023)
- [Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning](https://mlsanity.com/paper/2306.01150/) (2023)
- [Efficient Prompting via Dynamic In-Context Learning](https://mlsanity.com/paper/2305.11170/) (2023)

## Built on by (most cited first, 9 of 9)

- [A Survey of Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08748/) (2025)
- [ProCut: LLM Prompt Compression via Attribution Estimation](https://mlsanity.com/paper/2508.02053/) (2025)
- [SCOPE: A Generative Approach for LLM Prompt Compression](https://mlsanity.com/paper/2508.15813/) (2025)
- [Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression](https://mlsanity.com/paper/2602.08324/) (2026)
- [PIS: Linking Importance Sampling and Attention Mechanisms for Efficient Prompt Compression](https://mlsanity.com/paper/2504.16574/) (2025)
- [Cmprsr: Abstractive Token-Level Question-Agnostic Prompt Compressor](https://mlsanity.com/paper/2511.12281/) (2025)
- [WindowKV: Task-Adaptive Group-Wise KV Cache Window Selection for Efficient LLM Inference](https://mlsanity.com/paper/2503.17922/) (2025)
- [When Compression Becomes an Attack Surface: Black-Box Attacks on Prompt-Compressed LLM Agents](https://mlsanity.com/paper/2510.22963/) (2025)
- [Prompt Minimization: Reducing Input Redundancy Without Sacrificing Output Fidelity](https://mlsanity.com/paper/2609.31505/) (2026)

## Abstract (arXiv)

Prompt compression condenses contexts while maintaining their informativeness for different usage scenarios. It not only shortens the inference time and reduces computational costs during the usage of large language models, but also lowers expenses when using closed-source models. In a preliminary study, we discover that when instructing language models to compress prompts, different compression styles (e.g., extractive or abstractive) impact performance of compressed prompts on downstream tasks. Building on this insight, we propose Style-Compress, a lightweight framework that adapts a smaller language model to compress prompts for a larger model on a new task without additional training. Our approach iteratively generates and selects effective compressed prompts as task-specific demonstrations through style variation and in-context learning, enabling smaller models to act as efficient compressors with task-specific examples. Style-Compress outperforms two baseline compression models in four tasks: original prompt reconstruction, text summarization, multi-hop QA, and CoT reasoning. In addition, with only 10 samples and 100 queries for adaptation, prompts compressed by Style-Compress achieve performance on par with or better than original prompts at a compression ratio of 0.25 or 0.5.