| 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. |
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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. |
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. |
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 . |
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 . |
| Outcome: | The proposed method can be extended to controllability and toxicity reduction. |
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%. |
A Systematic Survey of Automatic Prompt Optimization Techniques (2025.emnlp-main)
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Kiran Ramnath, Kang Zhou, Sheng Guan, Soumya Smruti Mishra, Xuan Qi, Zhengyuan Shen, Shuai Wang, Sangmin Woo, Sullam Jeoung, Yawei Wang, Haozhu Wang, Han Ding, Yuzhe Lu, Zhichao Xu, Yun Zhou, Balasubramaniam Srinivasan, Qiaojing Yan, Yueyan Chen, Haibo Ding, Panpan Xu, Lin Lee Cheong
| Challenge: | Recent advances in prompt engineering have created impediments for end users to adopt . however, prompt engineering remains an impedance due to rapid advances in models, tasks, and associated best practices. |
| Approach: | They propose to define APO as a 5-part unifying framework and categorize all relevant works based on their salient features. |
| Outcome: | The proposed framework aims to improve the performance of large language models on various tasks. |
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 . |
| Outcome: | The proposed method reduces computations and memory costs by 27-90% . it retains 70-74% and 77-84% of the LLM capabilities at high compression ratios . |
Heuristic-based Search Algorithm in Automatic Instruction-focused Prompt Optimization: A Survey (2025.findings-acl)
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Wendi Cui, Jiaxin Zhang, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley A. Malin, Sricharan Kumar
| Challenge: | Recent advances in Large Language Models (LLMs) have led to remarkable achievements across a variety of NLP tasks. |
| Approach: | They propose a taxonomy of automatic prompt optimization methods that explore and improve prompts with minimal human oversight. |
| Outcome: | The proposed methods can explore and improve prompts with minimal human oversight. |
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. |
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression (2024.acl-long)
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| Challenge: | Longer prompts introduce irrelevant and redundant information, which can weaken LLMs' performance. |
| Approach: | They propose a prompt compression tool that improves LLMs' perception of key information in input prompts by up to 21.4% with around 4x fewer tokens in GPT-3.5-Turbo. |
| Outcome: | The proposed solution improves performance and reduces costs and latency by up to 21.4% with around 4x fewer tokens in the NaturalQuestions benchmark. |