Papers by Yu-Chen Lin
ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning (2024.findings-emnlp)
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| Challenge: | Prompt Tuning has been a popular fine-tuning method for large-scale pretrained language models. |
| Approach: | They propose a method that allows all soft prompts to share a set of learnable codebook vectors in each subspace, with each prompt differentiated by a number of adaptive weights. |
| Outcome: | The proposed method achieves superior performance on 17 diverse natural language tasks including natural language understanding (NLU) and question answering (QA) tasks by tuning only 0.3% of parameters of the PLMs. |
Linear Classifier: An Often-Forgotten Baseline for Text Classification (2023.acl-short)
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| Challenge: | Large-scale pre-trained language models such as BERT are popular solutions for text classification. |
| Approach: | They argue that large-scale pre-trained language models such as BERT are popular solutions for text classification . authors argue that running a simple baseline like linear classifiers on bag-of-words features is important for text classification . |
| Outcome: | The proposed approach may only sometimes get satisfactory results for some problems. |