LLM Feature Compression for Long-Sequence Text Processing
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Solution Overview
Problem
Existing large language models struggle with long-sequence processing capabilities, limiting their effectiveness in handling extensive information such as long documents, conversations, and code sequences, necessitating a method to enhance their ability to process inputs of arbitrary lengths.
Innovation Solution
A data processing method that involves obtaining feature representations from texts and prompts, compressing them at a target ratio, and using a large language model to generate accurate replies, allowing for dynamic-length text compression and adaptation without retraining, with controlled inference latency and memory consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models process long-sequence inputs directly, then processing accuracy is maintained, but processing time and memory consumption increase significantly
Solution Approach 1:
The patent segments long input sequences into multiple chunks or blocks that can be processed independently. Each chunk is processed separately through the large language model, and the results are then aggregated or combined to form the final output. This segmentation approach reduces the computational burden on the model at any given time while maintaining comprehensive processing of the entire long sequence.
Solution Approach 2:
The patent performs preliminary processing on long sequences before feeding them to the large language model. This may include preprocessing steps such as filtering, summarization, or feature extraction that reduce the sequence length or complexity in advance. By performing these actions beforehand, the model receives more manageable input without losing essential information, thus reducing processing time while maintaining accuracy.
2Measurement precision
If large language models process long-sequence inputs directly, then processing accuracy is maintained, but memory consumption increases significantly
Solution Approach 1:
The patent divides long sequences into smaller segments that can be processed with limited memory resources. Each segment is handled independently, requiring only a fraction of the total memory that would be needed to load the entire long sequence at once. This segmentation enables processing of arbitrarily long inputs within fixed memory constraints.
Solution Approach 2:
The patent extracts only the essential or relevant features from long sequences rather than processing the complete raw data. By identifying and extracting key information, the model reduces the amount of data that needs to be held in memory during processing, thereby reducing memory consumption while maintaining processing accuracy.
3Adaptability or versatility
If large language models are retrained for longer sequences, then processing capability is improved, but training time and computational resources increase
Solution Approach 1:
The patent uses copying techniques where pre-trained model weights and processing patterns are replicated and applied to long-sequence processing tasks without requiring full retraining. The model leverages its existing knowledge and processing capabilities, copying successful processing strategies from shorter sequences to handle longer ones, thus avoiding the need for time-consuming retraining while maintaining adaptability.
4Productivity
If compression is applied to feature representations, then processing efficiency is improved, but information loss increases
Solution Approach 1:
The patent dynamically adjusts compression parameters based on the specific characteristics of the input data and the processing requirements. By changing parameters such as compression ratio, precision levels, or approximation thresholds adaptively, the system optimizes the balance between compression efficiency and information preservation for different types of long sequences.
Solution Approach 2:
The patent implements feedback mechanisms that monitor the quality of compressed representations and adjust compression strategies accordingly. If information loss is detected to be excessive, the system reduces compression intensity or applies corrective transformations. This feedback loop ensures that compression maintains processing efficiency while keeping information loss within acceptable bounds.
Data Source
AI summary
A data processing method is provided, and relates to the field of artificial intelligence. The method includes: obtaining a first feature representation and a second feature representation, where the first feature representation is obtained by performing feature extraction on a first text, the second feature representation is obtained by performing feature extraction on a prompt, and the prompt indicates to perform compression at a target compression ratio; compressing the first feature representation and the second feature representation at the target compression ratio, to obtain compressed feature representations; and obtaining, based on the compressed feature representations, a second text by using a large language model, where the second text is used as a reply text to the first text.