# 500xCompressor: Generalized Prompt Compression for Large Language Models

arXiv: https://arxiv.org/abs/2408.03094 · HTML: https://mlsanity.com/paper/2408.03094/

## Key facts

- Cited by 47 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 17 of its 26 references that are in the map.
- First posted on arXiv 6 Aug 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, 17 of 17)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [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)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://mlsanity.com/paper/1704.04683/) (2017)
- [LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression](https://mlsanity.com/paper/2310.06839/) (2023)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [Learning to Compress Prompts with Gist Tokens](https://mlsanity.com/paper/2304.08467/) (2023)
- [Adapting Language Models to Compress Contexts](https://mlsanity.com/paper/2305.14788/) (2023)
- [LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression](https://mlsanity.com/paper/2403.12968/) (2024)
- [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension](https://mlsanity.com/paper/1910.09753/) (2019)
- [xRAG: Extreme Context Compression for Retrieval-augmented Generation with One Token](https://mlsanity.com/paper/2405.13792/) (2024)
- [Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models](https://mlsanity.com/paper/2210.03162/) (2022)
- [Tag-LLM: Repurposing General-Purpose LLMs for Specialized Domains](https://mlsanity.com/paper/2402.05140/) (2024)
- [Context Embeddings for Efficient Answer Generation in RAG](https://mlsanity.com/paper/2407.09252/) (2024)
- [Context Compression for Auto-regressive Transformers with Sentinel Tokens](https://mlsanity.com/paper/2310.08152/) (2023)

## Built on by (most cited first, 30 of 47)

- [LongCodeZip: Compress Long Context for Code Language Models](https://mlsanity.com/paper/2510.00446/) (2025)
- [A Survey of Multimodal Retrieval-Augmented Generation](https://mlsanity.com/paper/2504.08748/) (2025)
- [GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment](https://mlsanity.com/paper/2505.12215/) (2025)
- [PISCO: Pretty Simple Compression for Retrieval-Augmented Generation](https://mlsanity.com/paper/2501.16075/) (2025)
- [Autoencoding-Free Context Compression for LLMs via Contextual Semantic Anchors](https://mlsanity.com/paper/2510.08907/) (2025)
- [COMI: Coarse-to-fine Context Compression via Marginal Information Gain](https://mlsanity.com/paper/2602.01719/) (2026)
- [Latent Context Compilation: Distilling Long Context into Compact Portable Memory](https://mlsanity.com/paper/2602.21221/) (2026)
- [Embedding-to-Prefix: Parameter-Efficient Personalization for Pre-Trained Large Language Models](https://mlsanity.com/paper/2505.17051/) (2025)
- [Continuous Autoregressive Language Models](https://mlsanity.com/paper/2510.27688/) (2025)
- [Toward Reliable Context Compression for Long-Horizon Agents: An Empirical Study of Execution Instability](https://mlsanity.com/paper/2608.06503/) (2026)
- [Prompt-MII: Meta-Learning Instruction Induction for LLMs](https://mlsanity.com/paper/2510.16932/) (2025)
- [GradMem: Learning to Write Context into Memory with Test-Time Gradient Descent](https://mlsanity.com/paper/2603.13875/) (2026)
- [Beyond Hard and Soft: Hybrid Context Compression for Balancing Local and Global Information Retention](https://mlsanity.com/paper/2505.15774/) (2025)
- [A Survey on Prompt Tuning](https://mlsanity.com/paper/2507.06085/) (2025)
- [No Mean Feat: Simple, Strong Baselines for Context Compression](https://mlsanity.com/paper/2510.20797/) (2025)
- [PromptExp: Multi-granularity Prompt Explanation of Large Language Models](https://mlsanity.com/paper/2410.13073/) (2024)
- [ReasonGraph: Visualisation of Reasoning Paths](https://mlsanity.com/paper/2503.03979/) (2025)
- [PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning](https://mlsanity.com/paper/2505.09519/) (2025)
- [LoRA-Gen: Specializing Large Language Model via Online LoRA Generation](https://mlsanity.com/paper/2506.11638/) (2025)
- [CompLLM: Compression for Long Context Q&A](https://mlsanity.com/paper/2509.19228/) (2025)
- [Better Prompt Compression Without Multi-Layer Perceptrons](https://mlsanity.com/paper/2501.06730/) (2025)
- [Knowing When to Stop: Efficient Context Processing via Latent Sufficiency Signals](https://mlsanity.com/paper/2502.01025/) (2025)
- [Lossless Token Sequence Compression via Meta-Tokens](https://mlsanity.com/paper/2506.00307/) (2025)
- [Large Language Model as Token Compressor and Decompressor](https://mlsanity.com/paper/2603.25340/) (2026)
- [LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction](https://mlsanity.com/paper/2605.06676/) (2026)
- [End-to-End Context Compression at Scale](https://mlsanity.com/paper/2606.09659/) (2026)
- [BatchGEMBA: Token-Efficient Machine Translation Evaluation with Batched Prompting and Prompt Compression](https://mlsanity.com/paper/2503.02756/) (2025)
- [From Context to EDUs: Faithful and Structured Context Compression via Elementary Discourse Unit Decomposition](https://mlsanity.com/paper/2512.14244/) (2025)
- [ShortCoder: Knowledge-Augmented Syntax Optimization for Token-Efficient Code Generation](https://mlsanity.com/paper/2601.09703/) (2026)
- [Beyond Position Bias: Shifting Context Compression from Position-Driven to Semantic-Driven](https://mlsanity.com/paper/2605.09463/) (2026)

## Abstract (arXiv)

Prompt compression is crucial for enhancing inference speed, reducing costs, and improving user experience. However, current methods face challenges such as low compression ratios and potential data leakage during evaluation. To address these issues, we propose 500xCompressor, a method that compresses extensive natural language contexts into a minimum of one single special token. The 500xCompressor introduces approximately 0.3% additional parameters and achieves compression ratios ranging from 6x to 480x. It is designed to compress any text, answer various types of questions, and could be utilized by the original large language model (LLM) without requiring fine-tuning. Initially, 500xCompressor was pretrained on the Arxiv Corpus, followed by fine-tuning on the ArxivQA dataset, and subsequently evaluated on strictly unseen and classical question answering (QA) datasets. The results demonstrate that the LLM retained 62.26-72.89% of its capabilities compared to using non-compressed prompts. This study also shows that not all the compressed tokens are equally utilized and that K V values have significant advantages over embeddings in preserving information at high compression ratios. The highly compressive nature of natural language prompts, even for fine-grained complex information, suggests promising potential for future applications and further research into developing a new LLM language.