Is Memorization Context-Sensitive? Prefix-Based Extraction Beyond Isolated Prefixes
Quick summary
arXiv:2610.12085v1 Announce Type: new Abstract: Large language models (LLMs) can expose memorized training sequences under prefix-based extraction: given a prefix from a training example, the model may assign high probability to the original continuation. In deployed systems, however, prefixes are rarely evaluated in isolation. They often appear together with instructions, retrieved documents, or other task-specific context, as in retrieval-augmented generation (RAG). This motivates examining whether contextual conditioning mitigates memorization or merely changes the set of memorized samples
Key takeaways
- arXiv:2610.12085v1 Announce Type: new Abstract: Large language models (LLMs) can expose memorized training sequences under prefix-based extraction: given a prefix from a training example, the model may assign high probability to the original continuation.
- In deployed systems, however, prefixes are rarely evaluated in isolation.
- They often appear together with instructions, retrieved documents, or other task-specific context, as in retrieval-augmented generation (RAG).
Why it matters
“Is Memorization Context-Sensitive? Prefix-Based Extraction Beyond Isolated Prefixes” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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