Natural Language Prompt Compression for LLM Efficiency
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Solution Overview
Problem
Large language models (LLMs) face limitations due to constrained context lengths, which impede their ability to process lengthy documents and affect efficiency during inference. Additionally, compressed soft prompts lack transferability across different LLMs, limiting their utility.
Innovation Solution
A method for natural language prompt compression that involves obtaining an original prompt, providing it to an AI compression model, and generating a compressed prompt with fewer tokens. This method includes determining semantic loss between the original and compressed embeddings and training the AI compression model based on this loss, while maintaining the compressed prompt in a natural language format to ensure transferability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If the context length of LLMs is extended to process lengthy documents, then the ability to process large-scale datasets is improved, but the computational cost and inference time increase significantly
Solution Approach 1:
The patent extracts and separates the context processing function from the main LLM inference process by introducing a dedicated prompt compression model. This compression model processes the lengthy prompt separately before feeding it to the LLM, allowing the main model to operate with shorter, more efficient context windows while still capturing essential information from the original lengthy document.
2Productivity
If soft prompts are compressed into shorter tokens, then the processing efficiency is improved, but the transferability across different LLMs deteriorates
Solution Approach 1:
The patent creates a universal prompt compression model that can generate compressed prompts applicable across multiple different LLM architectures. The compression model is trained to preserve the essential semantic information and instructional content in a format that maintains effectiveness when transferred to various LLMs, making the compressed prompts universally applicable rather than model-specific.
Solution Approach 2:
The patent transforms the prompt representation by changing the tokenization parameters and compression ratio. By adjusting the compression level and token selection strategies, the system optimizes the balance between prompt length and information retention, creating compressed prompts that are sufficiently short for efficiency while retaining the semantic parameters needed for transferability across different models.
3Quantity of substance
If the prompt is compressed to reduce token count, then the computational resources required are reduced, but the semantic information may be lost
Solution Approach 1:
The patent implements a feedback mechanism during prompt compression where the compressed prompt is evaluated against the original prompt's semantic content. The compression model receives feedback on information retention quality and adjusts its compression strategy accordingly, preserving critical semantic elements while removing redundant tokens. This feedback loop ensures that compression does not sacrifice essential meaning.
Solution Approach 2:
The patent applies different compression strategies to different parts of the prompt based on their importance. Critical instructional sections and key contextual information are compressed with higher fidelity, while less important redundant content undergoes more aggressive compression. This local quality approach ensures that essential semantic information is preserved while achieving overall token reduction.
Data Source
AI summary
Methods, systems, and apparatuses for performing natural language prompt compression, the method being performed by an electronic device and including: obtaining a prompt for an artificial intelligence (AI) inference model, wherein the prompt corresponds to a first plurality of tokens; providing the prompt as an input to an AI compression model; and obtaining a compressed prompt based on an output of the AI compression model, wherein the compressed prompt corresponds to a second plurality of tokens which is smaller than the first plurality of tokens, and wherein the prompt and the compressed prompt are expressed using natural language.