The text discusses techniques to improve the efficiency of large language models (LLMs) through prompt compression, focusing on methods such as AutoCompressors and LongLLMLingua. The goal is to reduce inference costs and enable faster and accurate responses. The article compares different compression methods and concludes that LongLLMLingua shows promise for prompt compression in applications like Retrieval-Augmented Generation.
Accelerating Inference With Prompt Compression
Introduction
The inference process can be costly and time-consuming when using large language models, especially for longer inputs. This hinders their deployment in real-life applications, limiting their potential impact.
The Problem
Fast models tend to score lower in performance, leading to challenges in deploying them for practical use. The cost of inference throughput can limit the widespread use of large language models by individuals or small organizations.
The Solution
One practical and cost-effective method to address this issue is prompt compression. By compressing the original prompt while retaining important information, this technique speeds up the language model’s processing of inputs, enabling faster and accurate answers.
AutoCompressors
AutoCompressors summarize long text into short vector representations called summary vectors, acting as soft prompts for the model. These summary vectors are optimized end-to-end to best suit the specific task. They can be used for applications like Retrieval-Augmented Generation (RAG) to improve efficiency.
Selective Context
This method removes predictable tokens from the data by assigning self-information values to each lexical unit. It then retains only those from the first percentile, effectively compressing the prompt while maintaining context and reducing input tokens.
LongLLMLingua
LongLLMLingua improves upon LLMLingua by incorporating the user’s question into the compression process. It uses a question-aware coarse-to-fine compression method, document reordering, compression ratios, and post-compression subsequence recovery to enhance the language model’s perception of key information.
Practical Application
Using Nicolas Cage’s Wikipedia page as an example, we demonstrated how prompt compression techniques can significantly reduce input tokens while retaining essential information for the language model to generate accurate responses.
Conclusion
Of the methods discussed, LongLLMLingua seems to be the most promising for prompt compression in RAG applications, offering a 6–7x reduction in input tokens while retaining key information needed for accurate responses.
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