AI

Cache2LoRA: Understanding and Improving Context Internalization

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Briefings

Cache2LoRA introduces a method where each query aggregates evidence from a reusable frozen document cache before generating LoRA updates. This approach improves on existing context-to-LoRA methods across 17 tasks, achieving a macro F1 of 0.663. The system achieves decoding throughput 5.9 times that of full-context ICL at 32K documents and 256-token outputs with batch size 16. Generated LoRA preserves answer accuracy more reliably than the learned-KV variant under long, diverse intervening text.

Key points

• Cache2LoRA aggregates evidence from a reusable frozen document cache before generating LoRA updates for each query.
• The method achieves a macro F1 of 0.663 across 17 tasks, improving on existing context-to-LoRA methods.

Context

Context-to-LoRA methods encode documents as low-rank parameter updates, allowing language models to answer without retaining the source text in their decoding context. This study uses controlled diagnosis to separate fact discrimination, target retention, and response adaptation, revealing that target-only construction and memory-state optimization improve recovery at fixed update size.

Limitations

Decoding throughput is measured at specific batch size and document length; generated LoRA preserves accuracy more reliably than learned-KV variant under long, diverse intervening text. The study focuses on 17 tasks with macro F1 metrics and does not report per-task F1 values or specific failure modes under extreme conditions.

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