1 code implementation • 27 Mar 2024 • Brian Formento, Wenjie Feng, Chuan Sheng Foo, Luu Anh Tuan, See-Kiong Ng
Language models (LMs) are indispensable tools for natural language processing tasks, but their vulnerability to adversarial attacks remains a concern.
1 code implementation • 25 Mar 2024 • Nhat M. Hoang, Xuan Long Do, Duc Anh Do, Duc Anh Vu, Luu Anh Tuan
This draws a unique need for unified frameworks that can effectively detect and explain implicit toxic speech.
no code implementations • 19 Feb 2024 • Shuai Zhao, Leilei Gan, Luu Anh Tuan, Jie Fu, Lingjuan Lyu, Meihuizi Jia, Jinming Wen
Motivated by this insight, we developed a Poisoned Sample Identification Module (PSIM) leveraging PEFT, which identifies poisoned samples through confidence, providing robust defense against weight-poisoning backdoor attacks.
no code implementations • 11 Jan 2024 • Shuai Zhao, Meihuizi Jia, Luu Anh Tuan, Fengjun Pan, Jinming Wen
Our studies demonstrate that an attacker can manipulate the behavior of large language models by poisoning the demonstration context, without the need for fine-tuning the model.
1 code implementation • 12 Dec 2023 • Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Khoi Le, Zhiyuan Hu, Cong-Duy Nguyen, See-Kiong Ng, Luu Anh Tuan
Fully fine-tuning pretrained large-scale transformer models has become a popular paradigm for video-language modeling tasks, such as temporal language grounding and video-language summarization.
no code implementations • 5 Dec 2023 • Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Cong-Duy Nguyen, See-Kiong Ng, Luu Anh Tuan
Temporal Language Grounding seeks to localize video moments that semantically correspond to a natural language query.
no code implementations • 4 Dec 2023 • Cong-Duy Nguyen, The-Anh Vu-Le, Thong Nguyen, Tho Quan, Luu Anh Tuan
Language models have been supervised with both language-only objective and visual grounding in existing studies of visual-grounded language learning.
no code implementations • 4 Dec 2023 • Cong-Duy Nguyen, Thong Nguyen, Duc Anh Vu, Luu Anh Tuan
The effectiveness of a model is heavily reliant on the quality of the fusion representation of multiple modalities in multimodal sentiment analysis.
1 code implementation • 15 Nov 2023 • Guizhen Chen, Liying Cheng, Luu Anh Tuan, Lidong Bing
As large language models have demonstrated strong abilities in understanding context and generating natural language, it is worthwhile to evaluate the performance of LLMs on various computational argumentation tasks.
1 code implementation • 15 Nov 2023 • Yew Ken Chia, Guizhen Chen, Luu Anh Tuan, Soujanya Poria, Lidong Bing
Compared to the conventional chain of thought, our approach provides both valid and invalid reasoning demonstrations, to guide the model to reason step-by-step while reducing reasoning mistakes.
1 code implementation • 12 Oct 2023 • Haochen Li, Xin Zhou, Luu Anh Tuan, Chunyan Miao
In our proposed loss function, we apply three methods to estimate the weights of negative pairs and show that the vanilla InfoNCE loss is a special case of Soft-InfoNCE.
no code implementations • 2 May 2023 • Shuai Zhao, Jinming Wen, Luu Anh Tuan, Junbo Zhao, Jie Fu
Our method does not require external triggers and ensures correct labeling of poisoned samples, improving the stealthy nature of the backdoor attack.
no code implementations • 27 Feb 2023 • Anran Li, Rui Liu, Ming Hu, Luu Anh Tuan, Han Yu
Federated learning (FL) enables multiple data owners to build machine learning models collaboratively without exposing their private local data.
1 code implementation • 15 Nov 2022 • Leilei Gan, Baokui Li, Kun Kuang, Yating Zhang, Lei Wang, Luu Anh Tuan, Yi Yang, Fei Wu
Given the fact description text of a legal case, legal judgment prediction (LJP) aims to predict the case's charge, law article and penalty term.
1 code implementation • 5 Nov 2022 • Dang Minh Nguyen, Luu Anh Tuan
To the best of our knowledge, this is the first NLP defense that leverages the manifold structure against adversarial attacks.
1 code implementation • NeurIPS 2021 • Xinhsuai Dong, Luu Anh Tuan, Min Lin, Shuicheng Yan, Hanwang Zhang
The fine-tuning of pre-trained language models has a great success in many NLP fields.
1 code implementation • 7 Dec 2021 • Thong Nguyen, Luu Anh Tuan
Current state-of-the-art cross-lingual summarization models employ multi-task learning paradigm, which works on a shared vocabulary module and relies on the self-attention mechanism to attend among tokens in two languages.
1 code implementation • 22 Oct 2019 • Luu Anh Tuan, Darsh J Shah, Regina Barzilay
Automatic question generation can benefit many applications ranging from dialogue systems to reading comprehension.
no code implementations • 25 Sep 2019 • Yi Tay, Aston Zhang, Shuai Zhang, Alvin Chan, Luu Anh Tuan, Siu Cheung Hui
We propose R2D2 layers, a new neural block for training efficient NLP models.
1 code implementation • ACL 2019 • Yi Tay, Aston Zhang, Luu Anh Tuan, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, Siu Cheung Hui
Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient.
no code implementations • ACL 2019 • Yi Tay, Shuohang Wang, Luu Anh Tuan, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang
This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens.
1 code implementation • NeurIPS 2018 • Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Recurrent neural networks (RNNs) such as long short-term memory and gated recurrent units are pivotal building blocks across a broad spectrum of sequence modeling problems.
2 code implementations • NeurIPS 2018 • Yi Tay, Luu Anh Tuan, Siu Cheung Hui, Jian Su
Secondly, the dense connectors in our network are learned via attention instead of standard residual skip-connectors.
Ranked #2 on Question Answering on NewsQA
no code implementations • EMNLP 2018 • Yi Tay, Luu Anh Tuan, Siu Cheung Hui
This task enables many potential applications such as question answering and paraphrase identification.
no code implementations • 17 Jun 2018 • Yi Tay, Shuai Zhang, Luu Anh Tuan, Siu Cheung Hui
This paper has been withdrawn as we discovered a bug in our tensorflow implementation that involved accidental mixing of vectors across batches.
no code implementations • 3 Jun 2018 • Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Attention is typically used to select informative sub-phrases that are used for prediction.
no code implementations • ACL 2018 • Yi Tay, Luu Anh Tuan, Siu Cheung Hui, Jian Su
Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit.
no code implementations • 24 Mar 2018 • Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Similarly, we achieve competitive performance relative to AMANDA on the SearchQA benchmark and BiDAF on the NarrativeQA benchmark without using any LSTM/GRU layers.
Ranked #5 on Question Answering on RACE
2 code implementations • 28 Jan 2018 • Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Our model operates on a multi-hierarchical paradigm and is based on the intuition that not all reviews are created equal, i. e., only a select few are important.
no code implementations • EMNLP 2018 • Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Firstly, we introduce a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning.
Ranked #7 on Natural Language Inference on SciTail
1 code implementation • 21 Nov 2017 • Yi Tay, Luu Anh Tuan, Siu Cheung Hui
This paper explores the idea of learning temporal gates for sequence pairs (question and answer), jointly influencing the learned representations in a pairwise manner.
1 code implementation • 14 Nov 2017 • Yi Tay, Minh C. Phan, Luu Anh Tuan, Siu Cheung Hui
Our new method proposes a new \textsc{SkipFlow} mechanism that models relationships between snapshots of the hidden representations of a long short-term memory (LSTM) network as it reads.
Ranked #4 on Automated Essay Scoring on ASAP
no code implementations • 16 Aug 2017 • Yi Tay, Luu Anh Tuan, Minh C. Phan, Siu Cheung Hui
Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs.
1 code implementation • 25 Jul 2017 • Yi Tay, Luu Anh Tuan, Siu Cheung Hui
The dominant neural architectures in question answer retrieval are based on recurrent or convolutional encoders configured with complex word matching layers.
Ranked #1 on Question Answering on SemEvalCQA
1 code implementation • 17 Jun 2017 • Zhe Wang, Kingsley Kuan, Mathieu Ravaut, Gaurav Manek, Sibo Song, Yuan Fang, Seokhwan Kim, Nancy Chen, Luis Fernando D'Haro, Luu Anh Tuan, Hongyuan Zhu, Zeng Zeng, Ngai Man Cheung, Georgios Piliouras, Jie Lin, Vijay Chandrasekhar
Beyond that, we extend the original competition by including text information in the classification, making this a truly multi-modal approach with vision, audio and text.
no code implementations • TACL 2016 • Luu Anh Tuan, Siu Cheung Hui, See Kiong Ng
Taxonomies play an important role in many applications by organizing domain knowledge into a hierarchy of {`}is-a{'} relations between terms.