Papers by Yaru Hao
Knowledge Neurons in Pretrained Transformers (2022.acl-long)
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| Challenge: | Existing studies show that pretrained language models are good at recalling factual knowledge without fine-tuning. |
| Approach: | They propose a method to identify neurons that express factual knowledge in pretrained Transformers by filling-in-the-blank cloze queries. |
| Outcome: | The proposed method can be used to edit, erase, and update factual knowledge without fine-tuning. |
Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers (2023.findings-acl)
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| Challenge: | Large pretrained language models have shown surprising in-context learning ability . despite the great success in performance, its working mechanism remains unclear . |
| Approach: | They explain language models as meta-optimizers and understand in-context learning as implicit finetuning . they find that Transformer attention has a dual form of gradient descent . |
| Outcome: | The proposed model can predict labels for unseen inputs without parameter updates . the proposed model outperforms smaller models with a single parameter update . |
Investigating Learning Dynamics of BERT Fine-Tuning (2020.aacl-main)
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| Challenge: | Recent studies have shown that the fine-tuning process improves performance on downstream tasks. |
| Approach: | They propose two new pre-training tasks to improve the model performance on downstream tasks. |
| Outcome: | The proposed model achieves state-of-the-art on a wide array of NLP tasks. |
Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts (2026.acl-long)
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| Challenge: | Reinforcement learning with verifiable rewards (RLVR) training with Mixture-of-Experts policies remains fragile and prone to reward collapse. |
| Approach: | They propose a router shift-based policy optimization method that computes a per-token router-shift ratio conditioned on the previously activated experts and applies stop-gradient and a lower-bound floor. |
| Outcome: | The proposed method achieves better performance and greater stability than previous methods. |
Learning to Sample Replacements for ELECTRA Pre-Training (2021.findings-acl)
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| Challenge: | Experimental results show that ELECTRA pretrains a discriminator to detect replaced tokens . despite compelling performance, there is no direct feedback loop from discriminator and generator to generator, making replacements biased to correct tokens. |
| Approach: | They propose to augment sampling with a hardness prediction mechanism to encourage the discriminator to learn what it has not acquired. |
| Outcome: | The proposed method improves ELECTRA pre-training on various downstream tasks. |
Visualizing and Understanding the Effectiveness of BERT (D19-1)
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| Challenge: | Language model pre-training, such as BERT, has achieved strong performance in many NLP tasks. |
| Approach: | They propose to visualize loss landscapes and optimization trajectories of fine-tuning BERT on specific datasets. |
| Outcome: | The proposed model improves performance and generalization capability across tasks. |