Sentinel: Decoding Context Utilization via Attention Probing for Efficient LLM Context Compression
arXiv:2505.23277v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) often suffers from long and noisy retrieved contexts. Existing context compression methods typically rely on heuristic relevance estimation or supervised compression models rather than on how LLMs utilize retrieved context during inference. We propose Sentinel, a lightweight sentence-level compression framework that decodes inference-time contextual utilization behaviors from head-wise attention patterns of frozen LLMs. To ground supervision in retrieval-dependent answering behavior, Sentinel trains
The proliferation of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) drives the immediate need for more efficient context handling, directly addressed by innovations like Sentinel.
Improving LLM context compression directly enhances the efficiency, accuracy, and cost-effectiveness of AI applications, making advanced AI more accessible and performant for a wider range of tasks.
Context compression for RAG systems can become significantly more effective, moving beyond heuristics to decode how LLMs actually utilize information, leading to more robust and less resource-intensive AI deployments.
- · AI developers
- · Cloud providers
- · Enterprises adopting RAG
- · Inefficient RAG implementations
- · Manual context engineers
More accurate and cost-efficient RAG systems in production.
Accelerated development and broader adoption of complex AI applications leveraging extensive external knowledge.
Further democratization of advanced AI by lowering operational barriers and resource requirements.
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Read at arXiv cs.AI