Papers by Rongting Zhang
Order of Magnitude Speedups for LLM Membership Inference (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) are complex and require fine-tuning on proprietary datasets to improve performance and relevance. |
| Approach: | They propose a low-cost membership inference attack that leverages an ensemble of small quantile regression models to determine if a document belongs to the model’s training set. |
| Outcome: | The proposed approach achieves comparable or improved accuracy on fine-tuned LLMs of varying families and across multiple datasets. |