Compressing Context to Enhance Inference Efficiency of Large Language Models
Selective Context method enhances inference efficiency by compressing context, reducing context cost by 50%.
Key Findings
Methodology
Selective Context identifies and prunes redundancy in input context to make it more compact. It evaluates the informativeness of lexical units using self-information, retaining content with higher self-information to reduce computational cost.
Key Results
- Selective Context reduces context cost by 50%, inference memory usage by 36%, and inference time by 32%, with only a minor drop of 0.023 in BERTscore and 0.038 in faithfulness across four applications.
- Tested on arXiv papers, news articles, and long conversations, the method significantly reduces memory cost and generation latency.
- Compared to random deletion baseline, Selective Context significantly improves the quality of generated answers while retaining information.
Significance
This research improves inference efficiency by reducing computational demands when processing long documents and conversations with large language models. It addresses the bottleneck of long context processing without significantly sacrificing performance, reducing memory and time costs.
Technical Contribution
Selective Context introduces a model-agnostic approach that complements existing architecture optimization methods by pruning redundant context. Unlike sparse attention and local dense attention, it focuses on input context redundancy.
Novelty
Selective Context is the first method to enhance inference efficiency by pruning context redundancy, offering a new perspective compared to existing methods.
Limitations
- At high compression ratios, the quality of generated answers may degrade, especially in information-dense tasks.
- The method relies on self-information computation, which may limit adaptability to different language models.
Future Work
Future research could explore combining Selective Context with other architecture optimization methods to further enhance inference efficiency. Additionally, optimizing self-information computation across different languages and tasks is a potential direction.
AI Executive Summary
Large language models face increased computational demands when processing long documents and conversations, leading to inefficiencies. Existing methods like sparse attention and local dense attention focus on architecture optimization but fail to address context redundancy.
The Selective Context method identifies and prunes redundancy in input context, making it more compact and significantly reducing memory and time costs. Experimental results show that this method reduces context cost by 50%, inference memory usage by 36%, and inference time by 32% without significantly sacrificing performance.
This method offers a new perspective for improving inference efficiency in large language models, with significant academic and industrial implications. Future research could explore its combination with other architecture optimization methods and applications across different languages and tasks.
Deep Analysis
Background
Large language models excel in natural language processing tasks but face increased computational demands when processing long documents and conversations. Existing methods like sparse attention and local dense attention focus on architecture optimization but fail to address context redundancy.
Core Problem
Large language models face increased computational demands and potential context truncation when processing inputs exceeding fixed context lengths. Improving inference efficiency without significantly sacrificing performance is a key challenge.
Innovation
Selective Context identifies and prunes redundancy in input context, making it more compact. Unlike existing architecture optimization methods, it focuses on input context redundancy, offering a new perspective.
Methodology
- �� Evaluate informativeness of lexical units using self-information
- �� Retain content with higher self-information
- �� Prune redundant context to reduce computational cost
Experiments
Selective Context was tested on arXiv papers, news articles, and long conversations, with tasks including summarization, question answering, and conversation generation. Performance was evaluated using metrics like BERTscore and faithfulness.
Results
Selective Context reduces context cost by 50%, inference memory usage by 36%, and inference time by 32%, with only a minor drop of 0.023 in BERTscore and 0.038 in faithfulness.
Applications
This method can be applied to tasks requiring long context processing, such as document summarization, question answering, and conversation generation, significantly improving inference efficiency.
Limitations & Outlook
At high compression ratios, the quality of generated answers may degrade. The method relies on self-information computation, which may limit adaptability to different language models.
Plain Language Accessible to non-experts
Imagine a library with many books. A large language model is like a librarian who can quickly find the information you need. But if there are too many books, the librarian slows down. The Selective Context method is like an assistant who helps the librarian pick out the most important books so the librarian can find what you need faster.
ELI14 Explained like you're 14
Imagine you're playing a game with lots of tasks. A large language model is like your game character that helps you complete tasks. But when there are too many tasks, the character slows down. Selective Context is like an assistant that helps you pick the most important tasks so the character can complete them faster. Cool, right?
Glossary
Self-Information
A concept that measures the amount of information conveyed by an event; rare events have high self-information.
Used to evaluate the informativeness of lexical units.
BERTscore
A metric for evaluating text generation quality based on BERT model embeddings.
Used to evaluate the similarity between generated and reference texts.
Faithfulness
A metric that measures whether generated text accurately reflects input information.
Used to evaluate the accuracy of generated text.
Sparse Attention
A method to reduce attention computation cost by sparsifying the attention matrix.
Compared to Selective Context as an existing method.
Local Dense Attention
An optimization method for attention computation focusing on local information.
Compared to Selective Context as an existing method.
Open Questions Unanswered questions from this research
- 1 How to optimize self-information computation across different languages and tasks?
- 2 What causes performance degradation of Selective Context at high compression ratios?
Applications
Immediate Applications
Document Summarization
Improves efficiency in generating summaries for long documents, reducing computational costs.
Question Answering Systems
Reduces memory and time costs in long-text question answering tasks.
Long-term Vision
Conversation Generation
Improves efficiency in generating long conversations, applicable to chatbots and other applications.
Abstract
Large language models (LLMs) achieved remarkable performance across various tasks. However, they face challenges in managing long documents and extended conversations, due to significantly increased computational requirements, both in memory and inference time, and potential context truncation when the input exceeds the LLM's fixed context length. This paper proposes a method called Selective Context that enhances the inference efficiency of LLMs by identifying and pruning redundancy in the input context to make the input more compact. We test our approach using common data sources requiring long context processing: arXiv papers, news articles, and long conversations, on tasks of summarisation, question answering, and response generation. Experimental results show that Selective Context significantly reduces memory cost and decreases generation latency while maintaining comparable performance compared to that achieved when full context is used. Specifically, we achieve a 50\% reduction in context cost, resulting in a 36\% reduction in inference memory usage and a 32\% reduction in inference time, while observing only a minor drop of .023 in BERTscore and .038 in faithfulness on four downstream applications, indicating that our method strikes a good balance between efficiency and performance.