LLM memorization is context-sensitive, not erased by RAG

A new research paper explores whether context-sensitive conditioning affects the memorization of large language models (LLMs). The study investigates if adding instructions or retrieved documents, as seen in retrieval-augmented generation (RAG), mitigates memorization or simply alters which memorized sequences are extractable. Findings indicate that context does not eliminate memorization; instead, it reveals a core of robustly extractable memorized data and a sensitive boundary. While context can suppress some exposures and enable others, extractable memorized samples persist, posing continued security relevance. AI

IMPACT Contextual conditioning in LLMs does not eliminate memorization, indicating continued security risks even with RAG implementations.

RANK_REASON Academic paper on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Hugging Face
• large language models
• retrieval-augmented generation

• paper
• safety

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