Papers by Aaron Roth
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. |
Label Semantic Aware Pre-training for Few-shot Text Classification (2022.acl-long)
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| Challenge: | Existing models for text classification use label semantics but few studies have attempted to give models access to informative representations of labels. |
| Approach: | They propose to use label semantics to train generative models by performing secondary pre-training on labeled sentences from a variety of domains. |
| Outcome: | The proposed approach improves generalization and data efficiency of text classification systems while maintaining comparable performance to state-of-the-art models. |