Style-Compress: An LLM-Based Prompt Compression Framework Considering Task-Specific Styles (2024.findings-emnlp)
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| Challenge: | Prompt compression reduces inference time and costs while maintaining informativeness for different usage scenarios. |
| Approach: | They propose a framework that adapts a smaller language model to compress prompts for a larger model on a new task without additional training. |
| Outcome: | The proposed framework outperforms two baseline models in four tasks . iteratively generates and selects effective compressed prompts as task-specific demonstrations . |
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Prompt Compression for Large Language Models: A Survey (2025.naacl-long)
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| Challenge: | Current methods for improving LLM efficiency focus on optimizing the model itself, while prompt-centric methods focus on lowering the complexity of input. |
| Approach: | They propose to use prompt compression to optimize the compression encoder and combine hard and soft prompt methods to improve the efficiency of LLMs. |
| Outcome: | The proposed methods are categorized into hard prompt methods and soft prompt methods. |
Understanding and Improving Information Preservation in Prompt Compression for LLMs (2025.findings-emnlp)
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| Challenge: | Recent advances in large language models have enabled their successful application to a broad range of tasks. |
| Approach: | They propose a framework that allows for in-depth analysis of prompt compression methods. |
| Outcome: | The proposed framework analyzes state-of-the-art soft and hard compression methods . it shows that some fail to preserve key details from the original prompt, limiting performance on complex tasks. |
Perception Compressor: A Training-Free Prompt Compression Framework in Long Context Scenarios (2025.findings-naacl)
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| Challenge: | Long prompts contain redundant information and are sensitive to the position of key information in long context scenarios. |
| Approach: | They propose a training-free prompt compression framework that retains key information at token level while removing distracting tokens. |
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ProCut: LLM Prompt Compression via Attribution Estimation (2025.emnlp-industry)
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| Challenge: | ProCut compresses prompts using attribution analysis to reduce prompt size and latency. |
| Approach: | They propose a framework that compresses prompts through attribution analysis using a heuristic and attribution-based attribution model. |
| Outcome: | The proposed framework reduces prompt size by 78% while maintaining or improving task performance by 62%. |
Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models (2022.findings-emnlp)
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| Challenge: | We explore the idea of compressing the prompts used to condition language models. |
| Approach: | They explore the idea of compressing the prompts used to condition language models . they show that compressed prompts can retain a substantive amount of information about the original prompt . |
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LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly lengthy and require longer prompts . this paper presents a coarse-to-fine prompt compression method to reduce cost and increase performance. |
| Approach: | They propose a coarse-to-fine prompt compression method that maintains semantic integrity under high compression ratios and a token-level iterative compression algorithm to better model the interdependence between compressed contents. |
| Outcome: | The proposed method yields state-of-the-art performance and allows for up to 20x compression with little performance loss over four datasets from different scenarios. |
500xCompressor: Generalized Prompt Compression for Large Language Models (2025.acl-long)
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| Challenge: | Prompt compression is important for large language models to increase inference speed, reduce computation cost, and improve user experience. |
| Approach: | They propose a method that compresses natural language contexts into a special token . they propose to reduce computations and memory costs by reducing the complexity . |
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From Reading to Compressing: Exploring the Multi-document Reader for Prompt Compression (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have recently exhibited performance gains owing to a wide variety of prompting techniques, including Retrieval-Augmented Generation (RAG), Chain-of-Thought (CoT), and In-Context Learning (ICL). |
| Approach: | They propose a prompt compression method that captures the global context without compromising semantic consistency while detouring the necessity of pseudo-labels for training the compressor. |
| Outcome: | Empirical results show that the proposed method retains key contexts while reducing the prompt length by 80%. |
Learning to Compress Prompt in Natural Language Formats (2024.naacl-long)
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| Challenge: | Existing work rely on compressing long contexts into soft prompts, but soft prompt compression encounters limitations in transferability . natural language (NL) prompts are incompatible with back-propagation, and NL prompts lack flexibility in imposing length constraints. |
| Approach: | They propose a framework that compresses long prompts into NL formatted Capsule Prompts. |
| Outcome: | The proposed framework reduces 81.4% of the original length, decreases inference latency up to 4.5x, and saves 80.1% of budget overheads while providing transferability across diverse LLMs and different datasets. |
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression (2024.findings-acl)
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Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, Menglin Xia, Xufang Luo, Jue Zhang, Qingwei Lin, Victor Rühle, Yuqing Yang, Chin-Yew Lin, H. Vicky Zhao, Lili Qiu, Dongmei Zhang
| Challenge: | Existing approaches to compress prompts only leverage unidirectional context, causing suboptimal results. |
| Approach: | They propose a task-agnostic prompt compression method that takes tokens from context . they use a Transformer encoder to capture all essential information needed for prompt compression . |
| Outcome: | The proposed method is 3x-6x faster than existing prompt compression methods and faster than baselines. |