Papers by Nikhil Reddy Billa
Retracing the Past: LLMs Emit Training Data When They Get Lost (2025.emnlp-main)
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| Challenge: | Existing methods for extracting training data from large language models exhibit limited success . existing methods offer limited insight into the fundamental drivers of memorization leakage . |
| Approach: | They propose a framework for extracting memorized data by maximizing model uncertainty . they propose mismatched fine-tuning to weaken alignment and induce confusion . |
| Outcome: | The proposed attacks outperform baselines on unaligned and aligned LLMs . the proposed attacks exploit the model uncertainty of the input snippets induced by the model entropy spike . |