Previous Card
Titans: Learning to Memorize at Test Time
Don’t Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks
This paper introduces Cache-Augmented Generation (CAG) as an alternative to Retrieval-Augmented Generation (RAG) for knowledge tasks, leveraging long-context LLMs. CAG preloads relevant resources into the LLM's extended context and caches runtime parameters, eliminating real-time retrieval. This approach significantly reduces latency, minimizes retrieval errors, and simplifies system complexity while achieving comparable or superior performance for constrained knowledge bases. ✨
Article Points:
1
CAG bypasses real-time retrieval by preloading knowledge into LLM context.
2
CAG precomputes and caches LLM's KV parameters for inference.
3
CAG eliminates retrieval latency and minimizes retrieval errors.
4
CAG simplifies system architecture compared to RAG.
5
Experiments show CAG outperforms RAG in efficiency and accuracy for certain tasks.
6
CAG is optimal for scenarios with extensive, manageable reference contexts.
Don’t Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks
Challenges of RAG
Retrieval latency
Document selection errors
Increased system complexity
CAG Solution
Preload all relevant resources
Precompute KV cache
Utilize long-context LLMs
Methodology
External Knowledge Preloading
Inference with cached context
Efficient Cache Reset
Advantages of CAG
Reduced inference time
Unified context understanding
Simplified architecture
Experimental Results
Outperforms RAG in BERTScore
Faster generation time
Effective for manageable knowledge bases
Future Outlook