Auditing Training Data in Domain-adapted LLMs: LoRA-MINT

We present LoRA-MINT, a new methodology for Membership Inference Test (MINT) applied to recent Large Language Models (LLMs) fine-tuned for specific Natural Language Processing (NLP) tasks through Low-Rank Adaptation (LoRA). The primary goal is to assess whether individual samples were part of the training data of these adapted models, providing a useful auditing tool for the management of intellectual property and sensitive data. Our analysis explores the relationship between model perplexity an

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ethics.ai (5 June 2026), “Auditing Training Data in Domain-adapted LLMs: LoRA-MINT,” evidence record 1383, https://ethics.ai/record/1383 (originally published by arXiv).

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