Search Results for author: Anh Tuan Luu

Found 51 papers, 33 papers with code

Benchmarking Graph Neural Networks

16 code implementations2 Mar 2020 Vijay Prakash Dwivedi, Chaitanya K. Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, Xavier Bresson

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

Benchmarking Graph Classification +3

Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

1 code implementation3 Sep 2023 Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, Shuming Shi

While large language models (LLMs) have demonstrated remarkable capabilities across a range of downstream tasks, a significant concern revolves around their propensity to exhibit hallucinations: LLMs occasionally generate content that diverges from the user input, contradicts previously generated context, or misaligns with established world knowledge.

Hallucination World Knowledge

Holographic Factorization Machines for Recommendation

1 code implementation AAAI 2019 Yi Tay, Shuai Zhang, Anh Tuan Luu, Siu Cheung Hui, Lina Yao, Tran Dang Quang Vinh

Factorization Machines (FMs) are a class of popular algorithms that have been widely adopted for collaborative filtering and recommendation tasks.

Collaborative Filtering Retrieval

Recipe for a General, Powerful, Scalable Graph Transformer

3 code implementations25 May 2022 Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, Dominique Beaini

We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks.

Graph Classification Graph Property Prediction +4

Long Range Graph Benchmark

2 code implementations16 Jun 2022 Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, Dominique Beaini

Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer.

Benchmarking Graph Classification +4

Towards the TopMost: A Topic Modeling System Toolkit

1 code implementation13 Sep 2023 Xiaobao Wu, Fengjun Pan, Anh Tuan Luu

Topic models have been proposed for decades with various applications and recently refreshed by the neural variational inference.

Topic Models Variational Inference

A Survey on Neural Topic Models: Methods, Applications, and Challenges

2 code implementations27 Jan 2024 Xiaobao Wu, Thong Nguyen, Anh Tuan Luu

In this paper, we present a comprehensive survey on neural topic models concerning methods, applications, and challenges.

Topic Models

Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking

1 code implementation17 Jul 2017 Yi Tay, Anh Tuan Luu, Siu Cheung Hui

Our model, LRML (\textit{Latent Relational Metric Learning}) is a novel metric learning approach for recommendation.

 Ranked #1 on Recommendation Systems on Netflix (nDCG@10 metric)

Attribute Collaborative Ranking +2

Fact-Checking Complex Claims with Program-Guided Reasoning

1 code implementation22 May 2023 Liangming Pan, Xiaobao Wu, Xinyuan Lu, Anh Tuan Luu, William Yang Wang, Min-Yen Kan, Preslav Nakov

Fact-checking real-world claims often requires collecting multiple pieces of evidence and applying complex multi-step reasoning.

Fact Checking In-Context Learning

Contrastive Learning for Neural Topic Model

2 code implementations NeurIPS 2021 Thong Nguyen, Anh Tuan Luu

Recent empirical studies show that adversarial topic models (ATM) can successfully capture semantic patterns of the document by differentiating a document with another dissimilar sample.

Contrastive Learning Topic Models

Towards Robustness Against Natural Language Word Substitutions

1 code implementation ICLR 2021 Xinshuai Dong, Anh Tuan Luu, Rongrong Ji, Hong Liu

Robustness against word substitutions has a well-defined and widely acceptable form, i. e., using semantically similar words as substitutions, and thus it is considered as a fundamental stepping-stone towards broader robustness in natural language processing.

Natural Language Inference Sentiment Analysis

Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning

1 code implementation23 Nov 2022 Xiaobao Wu, Anh Tuan Luu, Xinshuai Dong

To overcome the data sparsity issue in short text topic modeling, existing methods commonly rely on data augmentation or the data characteristic of short texts to introduce more word co-occurrence information.

Contrastive Learning Data Augmentation

Zero-Shot Text Classification via Self-Supervised Tuning

1 code implementation19 May 2023 Chaoqun Liu, Wenxuan Zhang, Guizhen Chen, Xiaobao Wu, Anh Tuan Luu, Chip Hong Chang, Lidong Bing

In this work, we propose a new paradigm based on self-supervised learning to solve zero-shot text classification tasks by tuning the language models with unlabeled data, called self-supervised tuning.

Self-Supervised Learning Sentence +5

InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling

1 code implementation7 Apr 2023 Xiaobao Wu, Xinshuai Dong, Thong Nguyen, Chaoqun Liu, Liangming Pan, Anh Tuan Luu

Instead of the direct alignment in previous work, we propose a topic alignment with mutual information method.

Topic Models

Graph Transformers for Large Graphs

1 code implementation18 Dec 2023 Vijay Prakash Dwivedi, Yozen Liu, Anh Tuan Luu, Xavier Bresson, Neil Shah, Tong Zhao

As such, a key innovation of this work lies in the creation of a fast neighborhood sampling technique coupled with a local attention mechanism that encompasses a 4-hop reception field, but achieved through just 2-hop operations.

Graph Learning Graph Property Prediction +3

Certified Robustness Against Natural Language Attacks by Causal Intervention

1 code implementation24 May 2022 Haiteng Zhao, Chang Ma, Xinshuai Dong, Anh Tuan Luu, Zhi-Hong Deng, Hanwang Zhang

Deep learning models have achieved great success in many fields, yet they are vulnerable to adversarial examples.

Mercury: An Efficiency Benchmark for LLM Code Synthesis

1 code implementation12 Feb 2024 Mingzhe Du, Anh Tuan Luu, Bin Ji, See-Kiong Ng

Despite advancements in evaluating Large Language Models (LLMs) for code synthesis, benchmarks have predominantly focused on functional correctness, overlooking the importance of code efficiency.

Vision-and-Language Pretraining

1 code implementation5 Jul 2022 Thong Nguyen, Cong-Duy Nguyen, Xiaobao Wu, See-Kiong Ng, Anh Tuan Luu

Moreover, a list of training datasets and downstream tasks is supplied to further polish the perspective into V\&L pretraining.

Image Classification Machine Translation +4

Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction

1 code implementation22 May 2023 Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Anh Tuan Luu, Cong-Duy Nguyen, Zhen Hai, Lidong Bing

Multimodal Review Helpfulness Prediction (MRHP) aims to rank product reviews based on predicted helpfulness scores and has been widely applied in e-commerce via presenting customers with useful reviews.

From Static to Dynamic: A Continual Learning Framework for Large Language Models

1 code implementation22 Oct 2023 Mingzhe Du, Anh Tuan Luu, Bin Ji, See-Kiong Ng

The vast number of parameters in large language models (LLMs) endows them with remarkable capabilities, allowing them to excel in a variety of natural language processing tasks.

Continual Learning

How to train your draGAN: A task oriented solution to imbalanced classification

1 code implementation18 Nov 2022 Leon O. Guertler, Andri Ashfahani, Anh Tuan Luu

The long-standing challenge of building effective classification models for small and imbalanced datasets has seen little improvement since the creation of the Synthetic Minority Over-sampling Technique (SMOTE) over 20 years ago.

imbalanced classification

On the Affinity, Rationality, and Diversity of Hierarchical Topic Modeling

1 code implementation25 Jan 2024 Xiaobao Wu, Fengjun Pan, Thong Nguyen, Yichao Feng, Chaoqun Liu, Cong-Duy Nguyen, Anh Tuan Luu

Hierarchical topic modeling aims to discover latent topics from a corpus and organize them into a hierarchy to understand documents with desirable semantic granularity.

document understanding

ChatGPT as a Math Questioner? Evaluating ChatGPT on Generating Pre-university Math Questions

1 code implementation4 Dec 2023 Phuoc Pham Van Long, Duc Anh Vu, Nhat M. Hoang, Xuan Long Do, Anh Tuan Luu

In the context-unaware setting, we evaluate ChatGPT in generating math questions for each lesson from pre-university math curriculums that we crawl.

Arithmetic Reasoning Math +1

Learning to Attend via Word-Aspect Associative Fusion for Aspect-based Sentiment Analysis

1 code implementation14 Dec 2017 Yi Tay, Anh Tuan Luu, Siu Cheung Hui

Our novel model, \textit{Aspect Fusion LSTM} (AF-LSTM) learns to attend based on associative relationships between sentence words and aspect which allows our model to adaptively focus on the correct words given an aspect term.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1

PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation

1 code implementation14 Jun 2023 Zhiyuan Hu, Chumin Liu, Yue Feng, Anh Tuan Luu, Bryan Hooi

Controllable text generation is a challenging and meaningful field in natural language generation (NLG).

Denoising Sentence +1

CoupleNet: Paying Attention to Couples with Coupled Attention for Relationship Recommendation

no code implementations29 May 2018 Yi Tay, Anh Tuan Luu, Siu Cheung Hui

Our approach, the CoupleNet is an end-to-end deep learning based estimator that analyzes the social profiles of two users and subsequently performs a similarity match between the users.

Recommendation Systems

Holistic Multi-modal Memory Network for Movie Question Answering

no code implementations12 Nov 2018 Anran Wang, Anh Tuan Luu, Chuan-Sheng Foo, Hongyuan Zhu, Yi Tay, Vijay Chandrasekhar

In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop.

Question Answering Retrieval +1

Compositional De-Attention Networks

no code implementations NeurIPS 2019 Yi Tay, Anh Tuan Luu, Aston Zhang, Shuohang Wang, Siu Cheung Hui

Attentional models are distinctly characterized by their ability to learn relative importance, i. e., assigning a different weight to input values.

Machine Translation Natural Language Inference +4

Enriching and Controlling Global Semantics for Text Summarization

no code implementations EMNLP 2021 Thong Nguyen, Anh Tuan Luu, Truc Lu, Tho Quan

Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries.

Abstractive Text Summarization Text Generation

Grounding Language Representation with Visual Object Information via Cross Modal Pretraining

no code implementations29 Sep 2021 Cong-Duy T Nguyen, Anh Tuan Luu, Tho Quan

However, this approach has two main drawbacks: (i) the whole image usually contains more objects and backgrounds than the sentence itself; thus, matching them together will confuse the grounded model; (ii) CNN only extracts the features of the image but not the relationship between objects inside that, limiting the grounded model to learn complicated contexts.

Grounded language learning Object +1

Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation

no code implementations25 May 2023 Yandan Zheng, Anran Hao, Anh Tuan Luu

To alleviate the issues, we propose Jointprop, a Heterogeneous Graph-based Propagation framework for joint semi-supervised entity and relation extraction, which captures the global structure information between individual tasks and exploits interactions within unlabeled data.

named-entity-recognition Named Entity Recognition +3

Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System

1 code implementation16 Jun 2023 Zhiyuan Hu, Yue Feng, Anh Tuan Luu, Bryan Hooi, Aldo Lipani

This approach uses LLM as annotation-free user simulator to assess dialogue responses, combining them with smaller fine-tuned end-to-end TOD models.

Language Modelling Large Language Model

Enhancing Large Language Model Induced Task-Oriented Dialogue Systems Through Look-Forward Motivated Goals

no code implementations16 Sep 2023 Zhiyuan Hu, Yue Feng, Yang Deng, Zekun Li, See-Kiong Ng, Anh Tuan Luu, Bryan Hooi

Recently, the development of large language models (LLMs) has been significantly enhanced the question answering and dialogue generation, and makes them become increasingly popular in current practical scenarios.

Dialogue Generation Language Modelling +3

Topic Modeling as Multi-Objective Contrastive Optimization

no code implementations12 Feb 2024 Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Cong-Duy T Nguyen, See-Kiong Ng, Anh Tuan Luu

Secondly, we explicitly cast contrastive topic modeling as a gradient-based multi-objective optimization problem, with the goal of achieving a Pareto stationary solution that balances the trade-off between the ELBO and the contrastive objective.

Contrastive Learning Representation Learning +1

Don't Forget Your Reward Values: Language Model Alignment via Value-based Calibration

1 code implementation25 Feb 2024 Xin Mao, Feng-Lin Li, Huimin Xu, Wei zhang, Anh Tuan Luu

While Reinforcement Learning from Human Feedback (RLHF) significantly enhances the generation quality of Large Language Models (LLMs), recent studies have raised concerns regarding the complexity and instability associated with the Proximal Policy Optimization (PPO) algorithm, proposing a series of order-based calibration methods as viable alternatives.

Language Modelling

Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges

no code implementations5 Mar 2024 Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, Shafiq Joty

In the rapidly evolving field of machine learning (ML), data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection.

Data Augmentation

Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models

no code implementations15 Mar 2024 Chaoqun Liu, Wenxuan Zhang, Yiran Zhao, Anh Tuan Luu, Lidong Bing

We find that even though translation into English can help improve the performance of multilingual NLP tasks for English-centric LLMs, it may not be optimal for all scenarios.

Multilingual NLP

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