Papers by Rong Ge
Orthogonal Subspace Learning for Language Model Continual Learning (2023.findings-emnlp)
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| Challenge: | Existing methods for continual learning in language models suffer catastrophic forgetting when learning sequential tasks. |
| Approach: | They propose an orthogonal low-rank adaptation approach for continual learning in language models that uses orthogons to learn sequentially. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on continual learning benchmarks and preserves generalization ability of LLMs on unseen tasks. |
ReCaLL: Membership Inference via Relative Conditional Log-Likelihoods (2024.emnlp-main)
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| Challenge: | ReCaLL (Relative Conditional Log-Likelihood) is a membership inference attack that can detect LLMs’ pretraining data by leveraging their conditional language modeling capabilities. |
| Approach: | They propose a membership inference attack to detect LLMs’ pretraining data by leveraging their conditional language modeling capabilities. |
| Outcome: | The proposed model achieves state-of-the-art performance on the WikiMIA dataset, even with random and synthetic prefixes, and can be further improved using an ensemble approach. |
Do Transformers Parse while Predicting the Masked Word? (2023.emnlp-main)
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| Challenge: | Existing studies show that pre-trained language models encode linguistic structures like parse trees while being trained unsupervised. |
| Approach: | They propose to train pre-trained language models to encode linguistic structures like parse trees while unsupervised. |
| Outcome: | The proposed model performs optimally for masked language modeling loss on the English PCFG. |