Papers by June Yong Yang
Does it Really Generalize Well on Unseen Data? Systematic Evaluation of Relational Triple Extraction Methods (2022.naacl-main)
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| Challenge: | Existing extraction models memorize and recall already seen triples but cannot generalize effectively for unseen triples. |
| Approach: | They propose a method to generalize existing extraction models by rearranging datasets and augmenting test sets. |
| Outcome: | The proposed method can significantly increase the generalization performance of existing models. |
LRQ: Optimizing Post-Training Quantization for Large Language Models by Learning Low-Rank Weight-Scaling Matrices (2025.naacl-long)
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| Challenge: | Existing methods for quantizing weights and activations of large language models suffer from non-negligible accuracy drops, especially on massive multitask language understanding. |
| Approach: | They propose a weight-activation quantization method that reconstructs the outputs of an intermediate Transformer block by leveraging low-rank weight-scaling matrices. |
| Outcome: | The proposed method reduces the complexity of the weight-activation quantization techniques while achieving high throughput and reducing inference costs. |