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.
Outcome: The proposed framework outperforms existing methods on long context benchmarks.

Similar Papers

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 .
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.
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.
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 .
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.
Pretraining Context Compressor for Large Language Models with Embedding-Based Memory (2025.acl-long)

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Challenge: Efficient processing of long contexts in large language models is essential for real-world applications such as retrieval-augmented generation and in-context learning.
Approach: They propose a decoupled compressor-LLM framework that preserves contextual information within condensed embedding representations.
Outcome: The proposed framework outperforms baseline models in three domains and across eight datasets while adapting to different downstream LLMs.
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.
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%.
Retaining Key Information under High Compression Ratios: Query-Guided Compressor for LLMs (2024.acl-long)

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Challenge: Existing methods to compress long contexts have degraded dramatically as compression ratios increase, sometimes even falling to the closed-book level.
Approach: They propose a query-guided compression method that preserves key information within the compressed context.
Outcome: The proposed method can consistently perform well even at high compression ratios, and offers significant benefits in terms of inference cost and throughput.
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.

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