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      "statement": "# LLMLingua: budgeted coarse-to-fine prompt compression\n\nCombined a prompt-component budget controller, iterative token compression and small-model distribution alignment to compress inputs for an otherwise black-box language model.\n\n## Scientific scope\n\nInherits selective-context/self-information compression. Removing low-perplexity tokens does not guarantee preserved meaning or answers on arbitrary inputs. Compression-model tuning/compute costs remain and historical API price savings are not current prices.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nHuiqiang Jiang; Qianhui Wu; Chin-Yew Lin; Yuqing Yang; Lili Qiu\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models](https://arxiv.org/abs/2310.05736v1)\n",
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