Papers by Yulei Wu

3 papers
Sinkhorn Distance Minimization for Knowledge Distillation (2024.lrec-main)

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Challenge: Existing knowledge distillation methods investigate divergence measures but fail to deliver effective supervision when few distribution overlap exists between teacher and student.
Approach: They propose a knowledge distillation method that exploits the Sinkhorn distance to ensure a nuanced assessment of the disparity between teacher and student distributions.
Outcome: The proposed method outperforms state-of-the-art methods on all kinds of LLMs with encoder-only, encoder decoder, and decoded architectures.
Adversarial Text Generation by Search and Learning (2023.findings-emnlp)

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Challenge: Existing text generation methods only use heuristic replacement strategies or language models to generate replacement words at the word level.
Approach: They propose a search and learning framework for Adversarial Text Generation by Search and Learning to evaluate the robustness of natural language processing models.
Outcome: The proposed methods are significantly superior to the most advanced methods in terms of attack efficiency and adversarial text quality.
NL2Formula: Generating Spreadsheet Formulas from Natural Language Queries (2024.findings-eacl)

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Challenge: Creating spreadsheet formulas remains a tedious and error-prone task for many end-users . a novel task is proposed to generate spreadsheet formulae from a user's NL query .
Approach: They propose a task to generate formulas that are grounded on a spreadsheet table given a Natural Language query as input.
Outcome: The proposed task generates formulas that are grounded on a spreadsheet table, given a natural language query as input.

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