inLoRA-MINT: Internal Representations for Auditing Training Data in Domain-adapted LLMs
This work investigates the application of the Membership Inference Test (MINT) to large language models (LLMs) fine-tuned with LoRA, aiming to determine whether specific samples were used during training and address concerns about data privacy, leakage, and transparency. Building on our previous LoRA-MINT framework, wh...
Gonzalo Mancera, J. Fiérrez, Aythami Morales et al.
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