Papers by Wenbin Lai

2 papers
PDF-to-Tree: Parsing PDF Text Blocks into a Tree (2024.findings-emnlp)

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Challenge: Existing studies try to extract one universal reading order for PDF files, however, some applications, like Retrieval Augmented Generation, require breaking long articles into sections and subsections for better indexing.
Approach: They propose a new task and dataset, PDF-to-Tree, which organizes the text blocks of a PDF into a tree structure.
Outcome: The proposed parser achieves 93.93% accuracy, surpassing baseline methods by 6.72%.
Characterizing the Impacts of Instances on Robustness (2023.findings-acl)

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Challenge: Existing defense approaches focus on developing new model structures or training algorithms, but they do little to tap the potential of training instances.
Approach: They propose a method that can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.
Outcome: The proposed method can distinguish between robust and non-robust instances according to the model’s sensitivity to perturbations on individual instances during training.

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