Papers by David Ilić
Powerful Training-Free Membership Inference Against Fine-Tuned Autoregressive Language Models (2026.acl-long)
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| Challenge: | Existing methods for auditing fine-tuned language models have limited detection rates . membership inference attacks aim to determine if a specific record was in a model's training set . |
| Approach: | They propose a membership inference attack that exploits memorization at error positions . EZ-MIA achieves 3.8 higher detection than previous state-of-the-art . |
| Outcome: | The proposed attack achieves 3.8 higher detection than previous state-of-the-art models . EZ-MIA achieves 8 higher detectability than prior work, requiring no model training . |