LLMLingua is a suite of prompt compression techniques designed to enhance the efficiency and performance of large language models (LLMs) by reducing the length of input prompts without significant loss of information. By intelligently compressing prompts, LLMLingua addresses challenges such as increased computational costs, latency, and context window limitations associated with lengthy inputs. Key Features and Functionality: - Coarse-to-Fine Compression: Utilizes a budget controller to maintain semantic integrity under high compression ratios, ensuring essential information is preserved. - Token-Level Iterative Compression: Models interdependencies between compressed tokens to optimize prompt length while retaining meaning. - Instruction Tuning for Distribution Alignment: Aligns compressed prompts with the semantic distributions of LLMs to maintain performance across various tasks. - Task-Agnostic Compression: Applies to diverse scenarios, including reasoning, in-context learning, summarization, and dialogue, without the need for task-specific adjustments. - Integration with RAG Frameworks: Compatible with Retrieval-Augmented Generation frameworks like LangChain and LlamaIndex, facilitating seamless incorporation into existing workflows. Primary Value and User Solutions: LLMLingua significantly reduces the computational resources and time required for LLM inference by compressing prompts up to 20 times with minimal performance degradation. This compression allows for handling longer context inputs, overcoming context window limitations, and reducing API costs. By maintaining the reasoning and in-context learning capabilities of LLMs, LLMLingua ensures that users can achieve efficient and cost-effective model performance across various applications.