---
title: 'LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2310-05736-llmlingua.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu, LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: memory-and-context'
tags: arxiv, prompt-compression, efficiency, context
arxiv_id: 2310.05736
alias: 2310.05736-llmlingua
type: pointer
format: pointer
sources:
  - id: S1
    title: 'LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models'
    publisher: arXiv
    authors: 'Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, Lili Qiu'
    url: https://arxiv.org/abs/2310.05736
    version_read: 'v2 (6 Dec 2023)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models

## Summary
[LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://arxiv.org/abs/2310.05736) (2023) compresses prompts so that inference is cheaper while still keeping the information the model actually uses. It is a practical answer to long, expensive contexts when you would rather shrink the prompt than grow the window.

What it introduced: a prompt-compression method that uses a smaller language model to drop or shorten tokens that contribute little, with evaluations of speed and task quality after compression.

What it led to: later prompt- and context-compression work in agent systems, including compaction and gateway accounting. Live notes: [ao-cost-compression-accounting](https://iinventedlight.com/library/notes/ao-cost-compression-accounting/), [ao-compaction-summarize-and-resume](https://iinventedlight.com/library/notes/ao-compaction-summarize-and-resume/), and [ao-context-long-context-limits](https://iinventedlight.com/library/notes/ao-context-long-context-limits/).

Use it when you want to cut prompt tokens without training a new long-context model. Where to read it: https://arxiv.org/abs/2310.05736 (v2, last revised 6 Dec 2023). Accepted at EMNLP 2023.

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).