Papers by Tu Le
HiCOT: Improving Neural Topic Models via Optimal Transport and Contrastive Learning (2025.findings-acl)
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| Challenge: | Recent advances in neural topic models (NTMs) have improved topic quality but still face challenges: weak document-topic alignment, high inference costs due to large pretrained language models, and limited modeling of hierarchical topic structures. |
| Approach: | They propose a framework that integrates hierarchical clustering and contrastive learning to refine document-topic relationships using compact PLM-based embeddings. |
| Outcome: | The proposed framework improves topic coherence, topic performance, representation quality and computational efficiency over existing NTMs. |
EMO: Embedding Model Distillation via Intra-Model Relation and Optimal Transport Alignments (2025.emnlp-main)
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| Challenge: | Existing methods for knowledge distillation focus on direct output alignment, neglecting this crucial structural information. |
| Approach: | They propose a framework for knowledge distillation that maps tokens one-to-one and aligns attention matrix patterns using Centered Kernel Alignment. |
| Outcome: | The proposed framework significantly outperforms existing CTKD baselines. |
FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation (2024.findings-acl)
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Tu Vu, Mohit Iyyer, Xuezhi Wang, Noah Constant, Jerry Wei, Jason Wei, Chris Tar, Yun-Hsuan Sung, Denny Zhou, Quoc Le, Thang Luong
| Challenge: | Modern large language models often "hallucinate" plausible but factually incorrect information, which reduces their trustworthiness especially in settings where accurate and up-to-date information is critical. |
| Approach: | They develop a human evaluation procedure to measure correctness and hallucination and use it to benchmark both closed and open-source LLMs. |
| Outcome: | The proposed method outperforms both competing search engine-augmented prompting methods and commercial systems on search-augmented QA. |
STraTA: Self-Training with Task Augmentation for Better Few-shot Learning (2021.emnlp-main)
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| Challenge: | Recent advances in NLP demonstrate the effectiveness of applying large-scale pre-trained language models to downstream tasks. |
| Approach: | They propose a method that uses task augmentation to fine-tune unlabeled data. |
| Outcome: | The proposed approach improves sample efficiency across 12 few-shot benchmarks. |
Intent Classification and Slot Filling for Privacy Policies (2021.acl-long)
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| Challenge: | Sentences written in privacy policies explain privacy practices and the constituent text spans convey further specific information. |
| Approach: | They propose an English corpus of 5,250 intent and 11,788 slot annotations . they propose two alternative neural approaches to model the corpus as a sequence-to-sequence learning task. |
| Outcome: | The proposed corpus predicts intent classification and slot filling, while the sequence tagging method outperforms slot filler by a large margin. |