Papers by Anh Tuan Luu

Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification (D18-1)

Challenge:	Existing approaches to improve long-chain mathematical reasoning focus on the first erroneous step, but ignore all other steps and rely heavily on external signals.
Approach:	They propose a DPO framework that leverages step-wise rewards from the entire reasoning chain instead of optimizing only the first erroneous step.
Outcome:	The proposed framework improves on in-domain and out-of-domain mathematical reasoning benchmarks.

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Yi Tay, Anh Tuan Luu, Siu Cheung Hui, Jian Su

Information Retrieval

Challenge:	Existing sentiment lexicons do not handle word sense and the concept of semantic compositionality is non-existent in simple lexiconic approaches.
Approach:	They propose a lexicon-driven contextual attention mechanism and a contrastive co-attention mechanism that models contrasting polarities between all positive and negative words in a sentence.
Outcome:	The proposed model outperforms many other neural baselines on sentiment classification tasks on multiple benchmark datasets.

Understanding and Preventing Entropy Collapse in RLVR with On-Policy Entropy Flow Optimization (2026.findings-acl)

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Huimin Xu, Shuai Zhao, Xiaobao Wu, Anh Tuan Luu

Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension (D18-1)

Challenge:	Existing RLVR algorithms suffer from entropy collapse, leading to premature determinism and unstable optimization.
Approach:	They propose an adaptive entropy flow balancing mechanism that rescales entropic-increasing and enotro-decreazing updates according to their contributions to enthroy change.
Outcome:	The proposed method outperforms existing RLVR algorithms on six reasoning benchmarks.

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Yi Tay, Anh Tuan Luu, Siu Cheung Hui

Discrete Diffusion Language Model for Efficient Text Summarization (2025.findings-naacl)

Challenge:	Sequence encoders are crucial components in many neural architectures for learning to read and comprehend.
Approach:	They propose a compositional encoder that explicitly models across multiple granularities using a new dilated composition mechanism.
Outcome:	The proposed encoder is fast and expressive, and can model across multiple granularities.

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Do Huu Dat, Duc Anh Do, Anh Tuan Luu, Wray Buntine

Text Generation

Challenge:	Existing discrete diffusion models fail on conditional long-text generation due to incompatibility between the backbone architectures and the random noising process.
Approach:	They propose a semantic-aware noising process that enables Transformer backbones to handle long sequences effectively.
Outcome:	The proposed model outperforms existing models on three benchmark summarization datasets while achieving much faster inference speed compared to autoregressive models.

Unsupervised Hallucination Detection by Inspecting Reasoning Processes (2025.emnlp-main)

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Ponhvoan Srey, Xiaobao Wu, Anh Tuan Luu

Encoding and Controlling Global Semantics for Long-form Video Question Answering (2024.emnlp-main)

Challenge:	Unsupervised hallucination detection aims to identify hallucines generated by large language models without relying on labeled data.
Approach:	They propose an unsupervised method to detect hallucinated content by large language models . they use internal representations intrinsic to factual correctness to prompt the model to verify the truthfulness of a given statement .
Outcome:	The proposed framework outperforms existing unsupervised methods and is fully unsupervised and low cost.

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Thong Nguyen, Zhiyuan Hu, Xiaobao Wu, Cong-Duy Nguyen, See-Kiong Ng, Anh Tuan Luu

Multimodality

Challenge:	Existing methods to find answers for long videos fail to reason over the whole sequence of video, leading to sub-optimal performance.
Approach:	They propose a state space layer to integrate global semantics into video . they use a gating unit to enable controllability over the flow of global semantic into visual representations.
Outcome:	The proposed framework is able to integrate global semantics into visual representations.

Fact-Checking Complex Claims with Program-Guided Reasoning (2023.acl-long)

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Liangming Pan, Xiaobao Wu, Xinyuan Lu, Anh Tuan Luu, William Yang Wang, Min-Yen Kan, Preslav Nakov

Programming Languages in NLP

Challenge:	Fact-checking real-world claims often requires collecting multiple pieces of evidence and complex multi-step reasoning.
Approach:	They propose a novel fact-checking model that decomposes complex claims into simpler sub-tasks that can be solved using a shared library of specialized functions.
Outcome:	The proposed model outperforms seven baselines on two fact-checking datasets and has explicit output programs that benefit human debugging.

Exploring the Potential of Large Language Models in Computational Argumentation (2024.acl-long)

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Guizhen Chen, Liying Cheng, Anh Tuan Luu, Lidong Bing

Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learning (2022.emnlp-main)

Challenge:	Argumentation is an essential tool in various domains, including law, public policy, and artificial intelligence.
Approach:	They propose to evaluate LLMs on various computational argumentation tasks . they organize existing tasks into six main categories and standardize the format of 14 datasets .
Outcome:	The proposed model performs well on argument mining and argument generation tasks.

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Xiaobao Wu, Anh Tuan Luu, Xinshuai Dong

Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives (P19-1)

Challenge:	Existing topic models do not make full use of word co-occurrence information to model latent topics.
Approach:	They propose a novel short text topic modeling framework, Topic-Semantic Contrastive Topic Model, which uses augmented data and the data characteristic to learn the relations among samples.
Outcome:	The proposed framework outperforms state-of-the-art baselines regardless of the data augmentation availability, producing high-quality topics and topic distributions.

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Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang

Machine Reading Comprehension

Challenge:	Using a pointer-generator framework for reading/sampling over large documents, we propose a framework for learning over long narratives where documents easily span over thousands of tokens.
Approach:	They propose a curriculum learning (CL) based pointer-generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty.
Outcome:	The proposed framework improves on the NarrativeQA reading comprehension benchmark and reaches state-of-the-art performance.

UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource Languages (2024.acl-long)

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Trinh Pham, Khoi Le, Anh Tuan Luu

Gradient-Boosted Decision Tree for Listwise Context Model in Multimodal Review Helpfulness Prediction (2023.findings-acl)

Challenge:	Existing pre-trained language models are weak in addressing cross-lingual transfer tasks.
Approach:	They propose a method for initializing embeddings and choosing the right vocabulary size for cross-lingual systems.
Outcome:	The proposed method improves the F1-Score in several languages .

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Thong Nguyen, Xiaobao Wu, Xinshuai Dong, Cong-Duy Nguyen, Zhen Hai, Lidong Bing, Anh Tuan Luu

CodeArena: A Collective Evaluation Platform for LLM Code Generation (2025.acl-demo)

Challenge:	Existing studies have shown that FCNNs perform inefficient splitting for review features, making it difficult to clearly differentiate helpful from unhelpful reviews.
Approach:	They propose a listwise attention network that captures the MRHP ranking context and a pairwise optimization objective that enhances model generalization.
Outcome:	The proposed framework achieves state-of-the-art results and polished generalization performance on two large-scale MRHP benchmark datasets.

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Mingzhe Du, Anh Tuan Luu, Bin Ji, Xiaobao Wu, Yuhao Qing, Dong Huang, Terry Yue Zhuo, Qian Liu, See-Kiong Ng

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) have reshaped code generation, but persistent challenges impede accurate assessment.
Approach:	They propose an online evaluation framework tailored for large language models to assess their coding capabilities.
Outcome:	a new evaluation framework for large language models (LLMs) provides unbiased, unbiased evaluations and open access to solutions and test cases.

Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion (2024.findings-acl)

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Xiaobao Wu, Xinshuai Dong, Liangming Pan, Thong Nguyen, Anh Tuan Luu

Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Prediction (2022.emnlp-main)

Challenge:	Existing dynamic topic models lack the ability to reveal the evolution of topics . Existing models suffer from repetitive topic and unassociated topic issues .
Approach:	They propose a new evolution-tracking contrastive learning method that builds the similarity relations among dynamic topics and an unassociated word exclusion method to avoid unassociated topics.
Outcome:	The proposed model outperforms state-of-the-art models on downstream tasks and is robust to evolution intensities.

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Thong Nguyen, Xiaobao Wu, Anh Tuan Luu, Zhen Hai, Lidong Bing

P2P: A Poison-to-Poison Remedy for Reliable Backdoor Defense in LLMs (2026.findings-acl)

Challenge:	Modern review helpfulness prediction systems focus on polishing cross-modal representations and suffer from inferior optimization.
Approach:	They propose a method to polish cross-modal relation representations by learning mutual information through contrastive learning.
Outcome:	The proposed framework outperforms baselines and achieves state-of-the-art results on two publicly available datasets.

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Shuai Zhao, Xinyi Wu, Shiqian Zhao, Xiaobao Wu, Zhongliang Guo, Yanhao Jia, Anh Tuan Luu

Robustness in NLP

Challenge:	Defending Large Language Models (LLMs) against backdoors has long been trapped in a "cat-and-mouse" dilemma where defenders passively react to ever-shifting attack strategies.
Approach:	They propose a general and effective defense algorithm that implants benign triggers to reshape the model’s decision boundary.
Outcome:	The proposed defense algorithm can neutralize malicious backdoors while preserving task performance.

Universal Vulnerabilities in Large Language Models: Backdoor Attacks for In-context Learning (2024.emnlp-main)

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Shuai Zhao, Meihuizi Jia, Anh Tuan Luu, Fengjun Pan, Jinming Wen

Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives (2024.findings-acl)

Challenge:	In-context learning has shown high efficacy in several NLP tasks, especially in few-shot settings.
Approach:	They propose a backdoor attack method that poisons demonstration examples and poisons the demonstration context, preserving the model's generality.
Outcome:	The proposed method can make models behave in alignment with predefined intentions without fine-tuning the model.

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Thong Nguyen, Yi Bin, Junbin Xiao, Leigang Qu, Yicong Li, Jay Zhangjie Wu, Cong-Duy Nguyen, See-Kiong Ng, Anh Tuan Luu

Visual Data in NLP

Challenge:	Existing video-language understanding systems with human-like senses can mimic both our linguistic medium and visual environment with temporal dynamics.
Approach:	They propose to develop video-language understanding systems with human-like senses . they summarize their methods and highlight challenges associated with them .
Outcome:	The proposed models perform well in a variety of tasks and domains.

SemRoDe: Macro Adversarial Training to Learn Representations that are Robust to Word-Level Attacks (2024.naacl-long)

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Brian Formento, Wenjie Feng, Chuan-Sheng Foo, Anh Tuan Luu, See-Kiong Ng

Reasoning with Sarcasm by Reading In-Between (P18-1)

Challenge:	Existing approaches to defend against word-level attacks have been limited.
Approach:	They propose a new approach called Semantic Robust Defence to enhance the robustness of language models by aligning the domains with a distance-based objective.
Outcome:	The proposed approach can be generalized across word embeddings, even when they share minimal overlap at both vocabulary and word-substitution levels.

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Yi Tay, Anh Tuan Luu, Siu Cheung Hui, Jian Su

Sentiment Analysis

Challenge:	Sarcasm is a figurative speech act which manifests on social networks such as Twitter and Reddit.
Approach:	They propose a model that looks in-between rather than across to explicitly model contrast and incongruity.
Outcome:	The proposed model achieves state-of-the-art performance on all datasets and improves interpretability.

SCOPE: Compress Mathematical Reasoning Steps for Efficient Automated Process Annotation (2025.findings-acl)

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Huimin Xu, Xin Mao, Feng-Lin Li, Xiaobao Wu, Wang Chen, Wei Zhang, Anh Tuan Luu

Towards the TopMost: A Topic Modeling System Toolkit (2024.acl-demos)

Challenge:	Existing process annotation approaches are computationally expensive.
Approach:	They propose a compression-based approach that transforms reasoning steps into code and normalizes them through Abstract Syntax Tree.
Outcome:	The proposed method outperforms existing methods on Best-of-N strategy and ProcessBench.

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Xiaobao Wu, Fengjun Pan, Anh Tuan Luu

Don’t Forget Your Reward Values: Language Model Alignment via Value-based Calibration (2024.emnlp-main)

Challenge:	Current topic models adopt totally different datasets, implementations, and evaluations, hindering their research progress and applications.
Approach:	They propose a Topic Modeling System Toolkit that covers a broader spectrum of topic modeling scenarios with their complete lifecycles.
Outcome:	The proposed toolkit covers a broader spectrum of topic modeling scenarios with their complete lifecycles, including datasets, preprocessing, models, training, and evaluations.

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Xin Mao, Feng-Lin Li, Huimin Xu, Wei Zhang, Wang Chen, Anh Tuan Luu

MUR: Momentum Uncertainty guided Reasoning for Large Language Models (2026.acl-long)

Challenge:	Existing methods for generating large language models have been criticized for their complexity and instability.
Approach:	They propose a value-based calibration method to better align Large Language Models with human preferences.
Outcome:	The proposed method surpasses existing methods on AI assistant and summarization datasets, providing impressive generalizability, robustness, and diversity in different settings.

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Hang Yan, Fangzhi Xu, Rongman Xu, Yifei Li, Jian Zhang, Haoran Luo, Xiaobao Wu, Anh Tuan Luu, Haiteng Zhao, Qika Lin, Jun Liu

AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge (2025.acl-long)

Challenge:	Existing methods for optimizing reasoning quality are limited by overthinking.
Approach:	They propose a method that allocates thinking budgets to critical reasoning steps by tracking and aggregating step-wise uncertainty over time.
Outcome:	The proposed method reduces computation by over 45% on average while improving accuracy by 0.33–3.46%.

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Xiaobao Wu, Liangming Pan, Yuxi Xie, Ruiwen Zhou, Shuai Zhao, Yubo Ma, Mingzhe Du, Rui Mao, Anh Tuan Luu, William Yang Wang

Enriching and Controlling Global Semantics for Text Summarization (2021.emnlp-main)

Challenge:	Existing studies solve this challenge by updating benchmarks with newly collected data, but they fail to guarantee contamination-free evaluation as the newly collected knowledge may contain pre-existing knowledge.
Approach:	They propose an automated anti-leakage benchmarking framework that builds and updates benchmarks without human labor instead of using newly collected data.
Outcome:	The proposed framework significantly reduces the cost of benchmark maintenance to accommodate emerging LLMs.

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Thong Nguyen, Anh Tuan Luu, Truc Lu, Tho Quan

Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation (2023.acl-long)

Challenge:	Abstractive summarization models have been proven effective in creating fluent and informative summaries, but they suffer from the short-range dependency problem, causing them to produce summary that miss the key points of document.
Approach:	They propose a neural topic model empowered with normalizing flow to capture global semantics of the document and integrate them into the summarization model.
Outcome:	The proposed model outperforms state-of-the-art summarization models on five common text summarizing datasets, namely CNN/DailyMail, XSum, Reddit TIFU, arXiv, and PubMed.

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Yandan Zheng, Anran Hao, Anh Tuan Luu

Rethinking Reasoning: A Survey on Reasoning-based Backdoors in LLMs (2026.findings-acl)

Challenge:	Named Entity Recognition and Relation Extraction are two crucial tasks in Information Extraction.
Approach:	They propose a framework for joint semi-supervised entity and relation extraction that captures the global structure information between tasks and exploits interactions within unlabeled data.
Outcome:	The proposed framework outperforms state-of-the-art semi-supervised approaches on NER and RE tasks.

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Man Hu, Xinyi Wu, Zhufeng Suo, Jinbo Feng, Linghui Meng, Yanhao Jia, Anh Tuan Luu, Shuai Zhao

Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges (2024.findings-acl)

Challenge:	Recent models such as OpenAI o1 and DeepSeek-R1 produce explicit reasoning traces, often via Chain-of-Thought prompting.
Approach:	They propose a taxonomy that offers a unified perspective for summarizing existing approaches and categorizing reasoning-based backdoor attacks into associative, passive, and active.
Outcome:	The proposed taxonomy categorizes reasoning-based backdoor attacks into associative, passive, and active.

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Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, Shafiq Joty

Would you Rather? A New Benchmark for Learning Machine Alignment with Cultural Values and Social Preferences (2020.acl-main)

Challenge:	Data augmentation (DA) is a key technique for enhancing model performance by diversifying training examples without the need for additional data collection.
Approach:	They examine various strategies that utilize LLMs for data augmentation, including a novel exploration of learning paradigms where LLM-generated data is used for diverse forms of further training.
Outcome:	The proposed approach addresses the primary open challenges faced by LLMs in the field of large language models and aims to serve as a comprehensive guide for researchers and practitioners.

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Yi Tay, Donovan Ong, Jie Fu, Alvin Chan, Nancy Chen, Anh Tuan Luu, Chris Pal

Challenge:	Existing studies on optimal decision-making are limited and only consider individuals in isolation.
Approach:	They propose a task and corpus for learning alignments between machine and human preferences based on a gamified voting game .
Outcome:	The proposed task and corpus show that current state-of-the-art NLP models still leave much room for improvement.

Three Minds, One Legend: Jailbreak Large Reasoning Model with Adaptive Stacked Ciphers (2026.findings-acl)

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Nguyen Viet Anh, Shiqian Zhao, Gia Dao, Runyi Hu, Yi Xie, Xiaobao Wu, Anh Tuan Luu

Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning (2024.findings-naacl)

Challenge:	Existing jailbreak methods struggle to balance effectiveness with robustness against adaptive safety mechanisms.
Approach:	They propose a novel approach that targets Large Reasoning Models through an adaptive encryption pipeline designed to overwhelm their reasoning capabilities.
Outcome:	The proposed approach achieves an attack success rate of 85.6% on OpenAI GPT-o4-mini, outperforming state-of-the-art baselines by a significant margin of 17.2%.

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Shuai Zhao, Leilei Gan, Anh Tuan Luu, Jie Fu, Lingjuan Lyu, Meihuizi Jia, Jinming Wen

SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia (2025.findings-naacl)

Challenge:	Existing methods for parameter-efficient fine-tuning (PEFT) are not effective for weight-poisoning backdoor attacks.
Approach:	They propose a parameter-efficient fine-tuning (PEFT) method that updates only a limited set of model parameters and provides a robust defense against weight-poisoning backdoor attacks.
Outcome:	The proposed method identifies poisoned samples through confidence and is robust against weight-poisoning backdoor attacks.

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Chaoqun Liu, Wenxuan Zhang, Jiahao Ying, Mahani Aljunied, Anh Tuan Luu, Lidong Bing

Zero-Shot Text Classification via Self-Supervised Tuning (2023.findings-acl)

Challenge:	Large Language Models (LLMs) have shown remarkable performance across various English benchmarks, including both human exam datasets such as MMLU and instruction-following datasets.
Approach:	They introduce two new benchmarks to evaluate the capabilities of Large Language Models in Southeast Asian (SEA) application scenarios.
Outcome:	The proposed benchmarks show that they can discern LLM performance on SEA language tasks compared to their translated benchmarks.

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Chaoqun Liu, Wenxuan Zhang, Guizhen Chen, Xiaobao Wu, Anh Tuan Luu, Chip Hong Chang, Lidong Bing

Low-Resource NLP

Challenge:	Existing solutions to zero-shot text classification use pre-trained language models or large-scale annotated data.
Approach:	They propose a self-supervised learning paradigm to solve zero-shot text classification tasks by tuning the language models with unlabeled data.
Outcome:	The proposed model outperforms the state-of-the-art models on 7 out of 10 tasks and is less sensitive to prompt design.

Diffusion vs. Autoregressive Language Models: A Text Embedding Perspective (2025.emnlp-main)

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Siyue Zhang, Yilun Zhao, Liyuan Geng, Arman Cohan, Anh Tuan Luu, Chen Zhao

Compare, Compress and Propagate: Enhancing Neural Architectures with Alignment Factorization for Natural Language Inference (D18-1)

Challenge:	Large language model (LLM)-based embedding models surpass BERT and T5 on general-purpose text embeddable tasks.
Approach:	They propose to adopt diffusion language models for text embeddings to overcome limitations in unidirectional attention used during autoregressive pre-training.
Outcome:	The proposed model outperforms the existing LLM-based embedding model on reasoning tasks by 20% and 2% on traditional embeddable benchmarks.

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Yi Tay, Anh Tuan Luu, Siu Cheung Hui

Learning Uncertainty from Sequential Internal Dispersion in Large Language Models (2026.acl-long)

Challenge:	Using a new architecture, alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning.
Approach:	They propose a new architecture where alignment pairs are compared, compressed and then propagated to upper layers for enhanced representation learning.
Outcome:	The proposed architecture achieves competitive performance on three popular benchmarks, SNLI, MultiNLI and SciTail, while maintaining lightweight parameter size.

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Ponhvoan Srey, Xiaobao Wu, Cong-Duy T Nguyen, Anh Tuan Luu

Textual Manifold-based Defense Against Natural Language Adversarial Examples (2022.emnlp-main)

Challenge:	Recent approaches to detect hallucinations depend on model internal states to estimate uncertainty, but they focus on last or mean tokens.
Approach:	They propose a supervised hallucination detection framework that leverages token-wise, layer-wise features derived from hidden states.
Outcome:	The proposed framework outperforms baseline models and avoids large training sets.

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Dang Nguyen Minh, Anh Tuan Luu

Are LLMs Good Zero-Shot Fallacy Classifiers? (2024.emnlp-main)

Challenge:	Existing studies on adversarial images have shown that they leave the low-dimensional data manifold . Various defenses have been proposed to counter adversarials in NLP .
Approach:	They propose a defense mechanism that learns the embedding space manifold of the underlying language model and projects novel inputs back to the approximated structure before classification.
Outcome:	The proposed defense outperforms existing defenses under various attack settings while remaining unaffected to the clean accuracy.

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Fengjun Pan, Xiaobao Wu, Zongrui Li, Anh Tuan Luu

Low-Resource NLP

Challenge:	Existing fallacy classifiers lack sufficient labeled data for training, limiting their out-of-distribution (OOD) generalization abilities.
Approach:	They propose to use Large Language Models (LLMs) for zero-shot fallacy classification.
Outcome:	The proposed schemes outperform existing classifiers in OOD inference scenarios and opendomain tasks.

ChatKBQA: A Generate-then-Retrieve Framework for Knowledge Base Question Answering with Fine-tuned Large Language Models (2024.findings-acl)

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Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng, Yikai Guo, Wentai Zhang, Chenghao Ma, Guanting Dong, Meina Song, Wei Lin, Yifan Zhu, Anh Tuan Luu

Is Translation All You Need? A Study on Solving Multilingual Tasks with Large Language Models (2025.naacl-long)

Challenge:	Existing KBQA methods address inefficient knowledge retrieval and semantic parsing errors.
Approach:	They propose a generatethen-retrieve KBQA framework that generates logical form and replaces entities and relations with an unsupervised retrieval method to improve both generation and retrieval more directly.
Outcome:	Experimental results show that ChatKBQA achieves new state-of-the-art performance on standard KBQA datasets, WebQSP, and CWQ.

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Chaoqun Liu, Wenxuan Zhang, Yiran Zhao, Anh Tuan Luu, Lidong Bing

Enhancing Multimodal Entity Linking with Jaccard Distance-based Conditional Contrastive Learning and Contextual Visual Augmentation (2025.naacl-long)

Challenge:	Large language models (LLMs) have demonstrated multilingual capabilities, yet they are mostly English-centric due to the imbalanced training corpora.
Approach:	They extend the evaluation to real-world user queries and non-English-centric LLMs . they show that translation into English can boost LLM performance on NLP tasks .
Outcome:	The proposed evaluation extends to user queries and non-English-centric LLMs . it shows that translation into English can boost performance on NLP tasks, but not universally optimal .

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Cong-Duy T Nguyen, Xiaobao Wu, Thong Thanh Nguyen, Shuai Zhao, Khoi M. Le, Nguyen Viet Anh, Feng Yichao, Anh Tuan Luu

FineReason: Evaluating and Improving LLMs’ Deliberate Reasoning through Reflective Puzzle Solving (2025.acl-long)

Challenge:	Existing approaches to multimodal entity linking use contrastive learning to align input sentences and entities, but are limited by their random negative sampling.
Approach:	They propose a method to match negative samples with similar attributes using JD-CCL . they also propose 'contextual visual-aid controllable patch transform' experimental results demonstrate the strong effectiveness of their method .
Outcome:	The proposed method is able to match negative samples with similar attributes on a multimodal knowledge graph.

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Guizhen Chen, Weiwen Xu, Hao Zhang, Hou Pong Chan, Chaoqun Liu, Lidong Bing, Deli Zhao, Anh Tuan Luu, Yu Rong

Multi-expert Prompting Improves Reliability, Safety and Usefulness of Large Language Models (2024.emnlp-main)

Challenge:	Recent advances in large language models (LLMs) highlight an important shift from the “System 1” way of quick reactions to the “system 2” style of reflection-and-correction problem solving.
Approach:	They propose a logic-puzzle benchmark for systematic evaluation of large language models' reasoning capabilities that decomposes each puzzle into atomic steps.
Outcome:	The proposed model improves on state checking and state transition tasks and demonstrates gains in reasoning by up to 5.1%.

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Do Long, Duong Yen, Anh Tuan Luu, Kenji Kawaguchi, Min-Yen Kan, Nancy Chen

Unlearning Backdoor Attacks for LLMs with Weak-to-Strong Knowledge Distillation (2025.findings-acl)

Challenge:	Existing enhancements of ExpertPrompting improve the large language model generation process.
Approach:	They propose a novel enhancement of ExpertPrompting to improve LLM generation by simulating multiple experts, aggregating their responses and selecting the best among individual and aggregated responses.
Outcome:	The proposed enhancement outperforms ExpertPrompting and comparable baselines in truthfulness, factuality, informativeness, usefulness and harmfulness.

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Shuai Zhao, Xiaobao Wu, Cong-Duy T Nguyen, Yanhao Jia, Meihuizi Jia, Feng Yichao, Anh Tuan Luu

Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths (2024.findings-emnlp)

Challenge:	Parameter-efficient fine-tuning (PEFT) can bridge the gap between large language models and downstream tasks, but is vulnerable to malicious attacks.
Approach:	They propose a weak-to-strong unlearning algorithm based on feature alignment knowledge distillation to defend against backdoor attacks . they first train a small-scale language model through full-parameter fine-tuning to serve as the clean teacher model and then guide the large-scale poisoned student model in unlearning the backdoor.
Outcome:	The proposed method can unlearn backdoor features without compromising model performance.

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Yew Ken Chia, Guizhen Chen, Weiwen Xu, Anh Tuan Luu, Soujanya Poria, Lidong Bing

Extractive Summarization with Text Generator (2024.naacl-long)

Challenge:	Advanced models such as OpenAI o1 exhibit impressive problem-solving capabilities, but they may still falter on more complex problems, making errors that disrupt their reasoning paths.
Approach:	They propose a framework that encourages favorable branches at each reasoning step while penalizing unfavorable ones, enhancing the model’s overall problem-solving performance.
Outcome:	The proposed framework improves reasoning performance on multi-step reasoning tasks such as math word problems and science-based exam questions.

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Thang Le, Anh Tuan Luu

Text Generation

Challenge:	Existing extractive systems lack gold training signals, thereby hindering learning of extractive models.
Approach:	They propose to use text generators to train extractive summarizers by approximating outputs of abstractive summaries.
Outcome:	The proposed method can be used to train extractive summarizers without training . it is shown that the approximated summaries correlate positively with the auxiliary summary outputs.

Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks (P19-1)

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Yi Tay, Aston Zhang, Anh Tuan Luu, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, Siu Cheung Hui

Green & Sustainable NLP

Challenge:	Existing models for natural language processing are heavily parameterized and memory inefficient.
Approach:	They propose a series of lightweight and memory efficient neural architectures for NLP tasks . they propose quaternion algebra and hypercomplex spaces for computation .
Outcome:	The proposed models enable expressive inter-component interactions and significantly reduce parameter size without loss of performance.

KDMCSE: Knowledge Distillation Multimodal Sentence Embeddings with Adaptive Angular margin Contrastive Learning (2024.naacl-long)

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Cong-Duy Nguyen, Thong Nguyen, Xiaobao Wu, Anh Tuan Luu

SynTQA: Synergistic Table-based Question Answering via Mixture of Text-to-SQL and E2E TQA (2024.findings-emnlp)

Challenge:	Existing work on multimodal sentence embeddings took negative samples without reviewing, resulting in noisy and noisy negative samples.
Approach:	They propose a multimodal contrastive learning approach that inherits the knowledge from the teacher model to learn the difference between positive and negative instances.
Outcome:	The proposed approach can detect noisy and wrong negative samples before they are calculated in the contrastive objective.

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Siyue Zhang, Anh Tuan Luu, Chen Zhao

Multimodality

Challenge:	Text-to-SQL parsing and end-to end question answering have yet to be compared and their synergy remains unexplored.
Approach:	They propose a Synergistic Table-based Question Answering approach that integrates different models via answer selection.
Outcome:	The proposed approach improves on multiple benchmarks and on large scale datasets.

LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models (2025.acl-long)

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Zhiyuan Hu, Yuliang Liu, Jinman Zhao, Suyuchen Wang, WangYan WangYan, Wei Shen, Qing Gu, Anh Tuan Luu, See-Kiong Ng, Zhiwei Jiang, Bryan Hooi

Zero-to-Strong Generalization: Eliciting Strong Capabilities of Large Language Models Iteratively without Gold Labels (2025.coling-main)

Challenge:	Large language models face significant challenges in handling long-context tasks because of their limited effective context window size during pretraining, which restricts their ability to generalize over extended sequences.
Approach:	They propose a training strategy for extending the context window of LLMs including impactful token analysis, position index transformation, and training optimization strategies.
Outcome:	Experiments on three types of LLMs show that LongRecipe can utilize long sequences while requiring only 30% of the target context window size.

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Chaoqun Liu, Qin Chao, Wenxuan Zhang, Xiaobao Wu, Boyang Li, Anh Tuan Luu, Lidong Bing

Towards Fast and Accurate Modeling for Cross-Lingual Label Projection (2026.acl-long)

Challenge:	Pre-trained language models have demonstrated remarkable performance through supervised fine-tuning or in-context learning using gold labels.
Approach:	They propose a new paradigm termed zero-to-strong generalization that prompts LLMs to annotate unlabeled data and retain high-quality labels by filtering.
Outcome:	The proposed framework outperforms pre-trained language models on extensive classification and reasoning tasks on multiple model sizes.

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Thang Le, Huy Huu Nguyen, Anh Tuan Luu, Thamar Solorio, Thien Huu Nguyen

ClozeMath: Improving Mathematical Reasoning in Language Models by Learning to Fill Equations (2025.findings-acl)

Challenge:	Existing methods for label projection are inaccurate or slow for large-scale use.
Approach:	They propose to synthesize alignment sequence pairs and fine-tune an encoder model with span alignment objective while controlling data influence during training.
Outcome:	The proposed method outperforms state-of-the-art methods while maintaining fast inference speed across 50+ languages.

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Quang Hieu Pham, Thuy Duong Nguyen, Tung Pham, Anh Tuan Luu, Dat Quoc Nguyen

Who’s Who: Large Language Models Meet Knowledge Conflicts in Practice (2024.findings-emnlp)

Challenge:	Existing methods to train large language models do not capture how humans learn to think.
Approach:	They propose a method to fine-tune large language models for mathematical reasoning by using a text-infilling task that predicts masked equations from a given solution.
Outcome:	Experiments on GSM8K, MATH, and GSM-Symbolic show that ClozeMath surpasses baseline Masked Thought in performance and robustness with two test-time scaling decoding algorithms, Beam Search and Chain-of-Thought decoding.

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Quang Pham, Hoang Ngo, Anh Tuan Luu, Dat Quoc Nguyen

GeoPQA: Bridging the Visual Perception Gap in MLLMs for Geometric Reasoning (2025.findings-emnlp)

Challenge:	Recent large-scale pretrained language models excel in tasks requiring natural language understanding, but they often "hallucinate" plausible but incorrect content due to outdated or incorrect pretraining information.
Approach:	They propose a public benchmark dataset to examine model’s behavior in knowledge conflict situations.
Outcome:	The proposed model induces conflicts by asking about a common property among entities having the same name, resulting in questions with up to 8 distinctive answers.

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Guizhen Chen, Weiwen Xu, Hao Zhang, Hou Pong Chan, Deli Zhao, Anh Tuan Luu, Yu Rong

Multimodality

Challenge:	Recent advances in reinforcement learning (RL) have enhanced the reasoning abilities of large language models, but the impact on multimodal LLMs is limited.
Approach:	They propose a two-stage RL framework that enhances visual perception and fosters reasoning capabilities.
Outcome:	The proposed framework improves geometric reasoning by 9.7% and problem-solving by 9.1% compared to direct reasoning training approach.

Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences (D18-1)

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Yi Tay, Anh Tuan Luu, Siu Cheung Hui

ToXCL: A Unified Framework for Toxic Speech Detection and Explanation (2024.naacl-long)

Challenge:	a long standing problem in NLP research is learning a matching function between two text sequences . a deep architecture for this task is proposed by a team of researchers .
Approach:	They propose a new deep matching model using stacked recurrent encoders to learn affinity weights . they conduct extensive experiments on six well-studied text sequence matching datasets a plethora of applications are possible .
Outcome:	The proposed model improves performance on six well-studied text sequence matching datasets.

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Nhat Hoang, Do Long, Duc Anh Do, Duc Anh Vu, Anh Tuan Luu

AKEW: Assessing Knowledge Editing in the Wild (2024.emnlp-main)

Challenge:	Existing models that focus on explicit toxic speech detection and explanation are prone to error propagation problems . et al., 2018) show that toxic speech models can be prone for generating errors .
Approach:	They propose a framework that can detect and explain toxic speech using a target group generator and an encoder-decoder model.
Outcome:	The proposed model outperforms baseline models and achieves state-of-the-art effectiveness . the proposed model generates a toxic explanation that matches the ground truth explanation .

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Xiaobao Wu, Liangming Pan, William Yang Wang, Anh Tuan Luu

Beyond In-Context Learning: Aligning Long-form Generation of Large Language Models via Task-Inherent Attribute Guidelines (2025.findings-acl)

Challenge:	Recent Large Language Models (LLMs) have revolutionized the NLP field but their knowledge could become incorrect or outdated over time.
Approach:	They propose a new practical benchmark for knowledge editing that covers three editing settings of knowledge updates: structured facts, unstructured texts as facts, and extracted triplets.
Outcome:	The proposed method covers structured facts, unstructured texts as facts, and extracted triplets.

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Do Xuan Long, Duong Ngoc Yen, Do Xuan Trong, Anh Tuan Luu, Kenji Kawaguchi, Shafiq Joty, Min-Yen Kan, Nancy F. Chen

MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering (2025.findings-emnlp)

Challenge:	In-context learning is an important but not fully understood ability of pre-trained large language models.
Approach:	They propose a tool that generates two streams of guidelines capturing task language and format distributions and prompts them to define them by prompting.
Outcome:	The proposed model improves both strong open- and closed-source LLMs by over 5% in both zero- and few-shot settings.

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Siyue Zhang, Yuxiang Xue, Yiming Zhang, Xiaobao Wu, Anh Tuan Luu, Chen Zhao

Massively Multilingual Instruction-Following Information Extraction (2025.findings-acl)

Challenge:	Existing methods for answering time-sensitive questions lack temporal reasoning . existing methods struggle with these time-intensive questions, authors say .
Approach:	They propose a temporal-based question-answering framework that integrates temporal perturbations and gold evidence labels into a question processing framework.
Outcome:	The proposed framework outperforms baseline retrieval methods in retrieval performance.

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Thang Le, Huy Huu Nguyen, Anh Tuan Luu, Thien Huu Nguyen

Rewarding the Rare: Uniqueness-Aware RL for Creative Problem Solving in LLMs (2026.findings-acl)

Challenge:	Past literature on information extraction (IE) has focused on a few high-resource languages, hindering their applications on multilingual corpora.
Approach:	They propose a collection of data that unifies and standardizes instruction-following multilingual IE and introduce a structure-aware metric that captures partially matched spans.
Outcome:	The proposed framework standardizes and unifies 215 manually annotated datasets, covering 96 typologically diverse languages from 18 language families.

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Zhiyuan Hu, Yucheng Wang, Yufei He, Jiaying Wu, Yilun Zhao, See-Kiong Ng, Cynthia Breazeal, Anh Tuan Luu, Hae Won Park, Bryan Hooi