- Регистрация
- 1 Мар 2015
- Сообщения
- 11,740
- Баллы
- 155
This is a Plain English Papers summary of a research paper called . If you like these kinds of analysis, you should join or follow us on .
Overview
When you ask a large language model (LLM) a question that requires knowledge from documents, the traditional approach (RAG) retrieves relevant passages and adds them to the prompt. The problem is that this approach struggles with complex reasoning tasks that require connecting ...
Overview
- TASK introduces task-aware KV cache compression to improve LLM reasoning with large external documents
- Achieves 8.6x memory reduction while maintaining 95% performance
- Outperforms traditional RAG methods by embedding task-specific reasoning
- Automatically adapts compression based on document content and query needs
- Addresses the limitations of context windows in existing LLM systems
When you ask a large language model (LLM) a question that requires knowledge from documents, the traditional approach (RAG) retrieves relevant passages and adds them to the prompt. The problem is that this approach struggles with complex reasoning tasks that require connecting ...