Papers by Li Nguyen

Universal Proposition Bank 2.0 (2022.lrec-1)

Challenge:	Existing graph autoencoders and its variants have been used for node embedding . a new method is proposed to model consistency across different views of networks .
Approach:	They propose a network embedding method which enforces latent representations to be consistent across different views of networks by incorporating a multiview adversarial regularization module.
Outcome:	The proposed method compares favorably with the state-of-the-art methods on benchmark datasets and on a real-world application.

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Ishan Jindal, Alexandre Rademaker, Michał Ulewicz, Ha Linh, Huyen Nguyen, Khoi-Nguyen Tran, Huaiyu Zhu, Yunyao Li

Syntactic Text Processing

Challenge:	Semantic role labeling (SRL) is a shallow semantic parsing task that identifies "who did what to whom when, where etc." SRL is useful in a wide range of downstream NLP tasks and real-world applications.
Approach:	They propose a method to generate shallow semantic parsing tasks using monolingual SRL and multilingual parallel data.
Outcome:	The proposed method improves the quality of the generated propbanks.

Stronger, Lighter, Better: Towards Life-Long Attribute Value Extraction for E-Commerce Products (2024.findings-acl)

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Tao Zhang, Chenwei Zhang, Xian Li, Jingbo Shang, Hoang Nguyen, Philip Yu

Green & Sustainable NLP

Challenge:	Existing models for attribute value extraction struggle for parameter efficiency and reliability due to data contamination and catastrophic forgetting.
Approach:	They propose to decouple product type and attribute to promote de-contamination and parameter efficiency while scaling up.
Outcome:	The proposed model achieves state-of-the-art performance with affordable parameter size, least historical knowledge forgetting, and greatest robustness against noises.

CommonLID: Re-evaluating State-of-the-Art Language Identification Performance on Web Data (2026.acl-long)

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Pedro Ortiz Suarez, Laurie Burchell, Catherine Arnett, Rafael Mosquera, Sara Hincapié Monsalve, Thom Vaughan, Damian Stewart, Malte Ostendorff, Idris Abdulmumin, Vukosi Marivate, Shamsuddeen Hassan Muhammad, Atnafu Lambebo Tonja, Hend Al-Khalifa, Nadia Ghezaiel Hammouda, Verrah Akinyi Otiende, Tack Hwa Wong, Jakhongir Saydaliev, Melika Nobakhtian, Muhammad Ravi Shulthan Habibi, Chalamalasetti Kranti, Carol Muchemi, Khang Nguyen, Faisal Muhammad Adam, Luis Frentzen Salim, Reem Alqifari, Cynthia Jayne Amol, Joseph Marvin Imperial, Ilker Kesen, Ahmad Mustafid, Pavel Stepachev, Leshem Choshen, David Anugraha, Hamada Nayel, Seid Muhie Yimam, Vallerie Alexandra Putra, My Chiffon Nguyen, Azmine Toushik Wasi, Gouthami Vadithya, Rob Van Der Goot, Lanwenn ar C’horr, Karan Dua, Andrew Yates, Mithil Bangera, Yeshil Bangera, Hitesh Laxmichand Patel, Shu Okabe, Fenal Ashokbhai Ilasariya, Dmitry Gaynullin, Genta Indra Winata, Yiyuan Li, Juan Pablo Martínez, Amit Agarwal, Ikhlasul Akmal Hanif, Raia Abu Ahmad, Esther Adenuga, Filbert Aurelian Tjiaranata, Weerayut Buaphet, Michael Anugraha, Sowmya Vajjala, Benjamin L Rice, Azril Hafizi Amirudin, Jesujoba Oluwadara Alabi, Srikant Panda, Yassine Toughrai, Bruhan Kyomuhendo, Daniel Ruffinelli, null Akshata, Manuel Goulão, Ej Zhou, Ingrid Gabriela Franco Ramirez, Cristina Aggazzotti, Konstantin Dobler, Jun Kevin, Quentin Pagès, Nicholas Andrews, Nuhu Ibrahim, Mattes Ruckdeschel, Amr Keleg, Mike Zhang, Casper Rufaro Muziri, Saron Samuel, Sotaro Takeshita, Kun Kerdthaisong, Luca Foppiano, Rasul Dent, Tommaso Green, Ahmad Mustapha Wali, Kamohelo Makaaka, Vicky Feliren, Inshirah Idris, Hande Celikkanat, Abdulhamid Abubakar, Jean Maillard, Benoît Sagot, Thibault Clérice, Kenton Murray, Sarah K. K. Luger

Text Classification

Challenge:	Language identification (LID) is a fundamental step in curating multilingual corpora.
Approach:	They introduce CommonLID, a community-driven, human-annotated LID benchmark for the web domain, covering 109 languages.
Outcome:	The proposed benchmark covers 109 languages and shows that existing evaluations overestimate accuracy for many languages in the web domain.

CanVEC - the Canberra Vietnamese-English Code-switching Natural Speech Corpus (2020.lrec-1)

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Li Nguyen, Christopher Bryant

Multilinguality

Challenge:	Using monolingual tools, code-switching is a problem in the natural language processing community.
Approach:	They introduce the Canberra Vietnamese-English Code-switching corpus (CanVEC) which is an original corpus of mixed speech annotated with language information, part of speech tags and Vietnamese translations.
Outcome:	The proposed corpus was annotated with language information, part of speech tags and Vietnamese translations using pipelining and monolingual toolkits.

Doc2Bot: Accessing Heterogeneous Documents via Conversational Bots (2022.findings-emnlp)

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Haomin Fu, Yeqin Zhang, Haiyang Yu, Jian Sun, Fei Huang, Luo Si, Yongbin Li, Cam Tu Nguyen

Dialogue Systems & Conversational Agents

Challenge:	Documents contain various structures that hinder the ability of machines to comprehend . user information needs are often underspecified, and the nature of heterogeneous documents poses challenges.
Approach:	They propose a dataset for building machines that help users seek information via conversations . their dataset contains over 100,000 turns based on Chinese documents from five domains .
Outcome:	The proposed tasks are challenging and worthy of further research.

MemeQA: Holistic Evaluation for Meme Understanding (2025.acl-long)

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Khoi P. N. Nguyen, Terrence Li, Derek Lou Zhou, Gabriel Xiong, Pranav Balu, Nandhan Alahari, Alan Huang, Tanush Chauhan, Harshavardhan Bala, Emre Guzelordu, Affan Kashfi, Aaron Xu, Suyesh Shrestha, Megan Vu, Jerry Wang, Vincent Ng

LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)

Challenge:	Existing benchmarks for meme understanding only concern narrow aspects of meme semantics.
Approach:	They propose to use multiple-choice questions to evaluate meme comprehension . they use a dataset of over 9,000 multiple-question questions to assess meme comprehension.
Outcome:	The proposed model outperforms existing models on meme comprehension . the model makes many errors on memes where proper understanding requires going beyond sentiment .

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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu

ParaICL: Towards Parallel In-Context Learning (2025.naacl-long)

Challenge:	Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents.
Approach:	They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety.
Outcome:	The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system.

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Xingxuan Li, Xuan-Phi Nguyen, Shafiq Joty, Lidong Bing

From Selection to Generation: A Survey of LLM-based Active Learning (2025.acl-long)

Challenge:	Existing methods to improve ICL performance are limited by the length of the input context.
Approach:	They propose a method that utilizes all demonstration examples without exceeding the manageable context length.
Outcome:	The proposed method can be scaled up to integrate with existing methods.

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Yu Xia, Subhojyoti Mukherjee, Zhouhang Xie, Junda Wu, Xintong Li, Ryan Aponte, Hanjia Lyu, Joe Barrow, Hongjie Chen, Franck Dernoncourt, Branislav Kveton, Tong Yu, Ruiyi Zhang, Jiuxiang Gu, Nesreen K. Ahmed, Yu Wang, Xiang Chen, Hanieh Deilamsalehy, Sungchul Kim, Zhengmian Hu, Yue Zhao, Nedim Lipka, Seunghyun Yoon, Ting-Hao Kenneth Huang, Zichao Wang, Puneet Mathur, Soumyabrata Pal, Koyel Mukherjee, Zhehao Zhang, Namyong Park, Thien Huu Nguyen, Jiebo Luo, Ryan A. Rossi, Julian McAuley

Responsible & Trustworthy NLP

Challenge:	Large Language Models (LLMs) have been used for selection and training of data for active learning.
Approach:	They propose an intuitive taxonomy that categorizes LLM-based active learning techniques and discuss the transformative roles they can play in the active learning loop.
Outcome:	The proposed model can generate entirely new data instances and provide more cost-effective annotations with fewer labeled data instances.

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

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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.

Improving Question Generation with Multi-level Content Planning (2023.findings-emnlp)

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Zehua Xia, Qi Gou, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li, Nguyen Cam-Tu

Text Generation

Challenge:	Existing studies suggest key phrase selection is essential for question generation, yet it is difficult to connect disjointed phrases into meaningful questions, especially for long context.
Approach:	They propose a QG framework that uses multi-level content planning to generate questions from a given context and an answer.
Outcome:	The proposed framework outperforms baselines on two popular QG datasets.

How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts (2026.findings-acl)

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Minh-Vuong Nguyen, Fatemeh Shiri, Zhuang Li, Karin Verspoor

Robustness in NLP

Challenge:	Existing evaluations of Large Language Models for clinical numerical reasoning provide limited operation-level coverage and limited robustness of numerical understanding across clinical note formats.
Approach:	They propose a benchmarking tool that evaluates four main types of clinical numeracy . they present longitudinal MIMIC-IV vital-sign records in three semantically equivalent representations .
Outcome:	The proposed benchmark evaluates four main types of clinical numeracy: value retrieval, arithmetic computation, relational comparison, and aggregation.

End-to-End Evaluation for Low-Latency Simultaneous Speech Translation (2023.emnlp-demo)

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Christian Huber, Tu Anh Dinh, Carlos Mullov, Ngoc-Quan Pham, Thai Binh Nguyen, Fabian Retkowski, Stefan Constantin, Enes Ugan, Danni Liu, Zhaolin Li, Sai Koneru, Jan Niehues, Alexander Waibel

Speech & Audio in NLP

Challenge:	a framework to evaluate low-latency speech translations is currently only limited to specific aspects and is not able to compare different approaches.
Approach:	They propose a framework to perform and evaluate low-latency speech translation in realistic conditions.
Outcome:	The proposed framework evaluates various aspects of low-latency speech translation under realistic conditions.

Schema-Guided Culture-Aware Complex Event Simulation with Multi-Agent Role-Play (2024.emnlp-demo)

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Sha Li, Revanth Gangi Reddy, Khanh Nguyen, Qingyun Wang, Yi Fung, Chi Han, Jiawei Han, Kartik Natarajan, Clare Voss, Heng Ji

Challenge:	Complex news events require swift responses from government and society, authors say . relying on historical events to project the future is insufficient, they say - a simulator for complex news events is needed .
Approach:	They propose a controllable complex news event simulator guided by event schema and user-provided assumptions . they incorporate a geo-diverse commonsense and cultural norm-aware knowledge enhancement component .
Outcome:	The proposed simulator achieves higher coherence and appropriateness than existing models.

RuleR: Improving LLM Controllability by Rule-based Data Recycling (2025.naacl-short)

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Ming Li, Han Chen, Chenguang Wang, Dang Nguyen, Dianqi Li, Tianyi Zhou

Challenge:	Existing supervised fine-tuning datasets are composed of general instructions without userspecified constraints.
Approach:	They propose a data augmentation method incorporating multiple constraints into the original data samples according to predefined rules to create new training tasks.
Outcome:	The proposed method improves LLM controllability while maintaining general instruction-following capabilities.

Automated Generation of Accurate & Fluent Medical X-ray Reports (2021.emnlp-main)

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Hoang Nguyen, Dong Nie, Taivanbat Badamdorj, Yujie Liu, Yingying Zhu, Jason Truong, Li Cheng

RESIN-EDITOR: A Schema-guided Hierarchical Event Graph Visualizer and Editor (2023.emnlp-demo)

Challenge:	Existing medical report generation efforts focus on producing human-readable reports, yet the generated text may not be well aligned to the clinical facts.
Approach:	They propose to automate the generation of medical reports from chest X-ray image inputs . medical reports are the primary medium, which physicians communicate findings from scans - authors say .
Outcome:	The proposed method achieves fluency and clinical accuracy on common metrics.

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Khanh Duy Nguyen, Zixuan Zhang, Reece Suchocki, Sha Li, Martha Palmer, Susan Windisch Brown, Jiawei Han, Heng Ji

MoVa: Towards Generalizable Classification of Human Morals and Values (2025.emnlp-main)

Challenge:	Existing IE tools for atomic events are limited when applied to such complex events.
Approach:	They propose to use event schemas to guide the organization of complex events and to edit hierarchical graphs.
Outcome:	The proposed tool outperforms existing IE visualization tools in both IE result analysis and general model improvements.

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Ziyu Chen, Junfei Sun, Chenxi Li, Tuan Dung Nguyen, Jing Yao, Xiaoyuan Yi, Xing Xie, Chenhao Tan, Lexing Xie

Text Classification

Challenge:	Identifying human morals and values embedded in language is essential to empirical studies of communication.
Approach:	They propose a framework for generalizable classification of human morals and values . they recommend a classification strategy that scores all related concepts simultaneously .
Outcome:	The proposed method outperforms fine-tuned models across domains and frameworks.

Differentiable Window for Dynamic Local Attention (2020.acl-main)

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Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, Xiaoli Li

Challenge:	Existing general purpose components for learning differentiable windows are hard to optimize.
Approach:	They propose a new neural module and general purpose component for dynamic window selection that can enable more focused attentions over the input regions.
Outcome:	The proposed approach improves on a myriad of NLP tasks including machine translation, sentiment analysis, subject-verb agreement and language modeling.

KG-RAG: Enhancing GUI Agent Decision-Making via Knowledge Graph-Driven Retrieval-Augmented Generation (2025.emnlp-main)

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Ziyi Guan, Jason Chun Lok Li, Zhijian Hou, Pingping Zhang, Donglai Xu, Yuzhi Zhao, Mengyang Wu, Jinpeng Chen, Thanh-Toan Nguyen, Pengfei Xian, Wenao Ma, Shengchao Qin, Graziano Chesi, Ngai Wong

Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia (2025.acl-long)

Challenge:	Recent advances in GUI agents have limited app-specific knowledge of complex mobile tasks.
Approach:	They propose a Knowledge Graph-driven Retrieval-Augmented Generation framework that transforms fragmented UTGs into structured vector databases for efficient real-time retrieval.
Outcome:	The proposed framework outperforms existing methods in a 75.8% success rate and 84.6% decision accuracy test across mobile apps.

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Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz, Tack Hwa Wong, Mohammad Rifqi Farhansyah, Thant Thiri Maung, Frederikus Hudi, David Anugraha, Muhammad Ravi Shulthan Habibi, Muhammad Reza Qorib, Amit Agarwal, Joseph Marvin Imperial, Hitesh Laxmichand Patel, Vicky Feliren, Bahrul Ilmi Nasution, Manuel Antonio Rufino, Genta Indra Winata, Rian Adam Rajagede, Carlos Rafael Catalan, Mohamed Fazli Mohamed Imam, Priyaranjan Pattnayak, Salsabila Zahirah Pranida, Kevin Pratama, Yeshil Bangera, Adisai Na-Thalang, Patricia Nicole Monderin, Yueqi Song, Christian Simon, Lynnette Hui Xian Ng, Richardy Lobo Sapan, Taki Hasan Rafi, Bin Wang, null Supryadi, Kanyakorn Veerakanjana, Piyalitt Ittichaiwong, Matthew Theodore Roque, Karissa Vincentio, Takdanai Kreangphet, Phakphum Artkaew, Kadek Hendrawan Palgunadi, Yanzhi Yu, Rochana Prih Hastuti, William Nixon, Mithil Bangera, Adrian Xuan Wei Lim, Aye Hninn Khine, Hanif Muhammad Zhafran, Teddy Ferdinan, Audra Aurora Izzani, Ayushman Singh, Evan Evan, Jauza Akbar Krito, Michael Anugraha, Fenal Ashokbhai Ilasariya, Haochen Li, John Amadeo Daniswara, Filbert Aurelian Tjiaranata, Eryawan Presma Yulianrifat, Can Udomcharoenchaikit, Fadil Risdian Ansori, Mahardika Krisna Ihsani, Giang Nguyen, Anab Maulana Barik, Dan John Velasco, Rifo Ahmad Genadi, Saptarshi Saha, Chengwei Wei, Isaiah Edri W. Flores, Kenneth Chen Ko Han, Anjela Gail D. Santos, Wan Shen Lim, Kaung Si Phyo, Tim Santos, Meisyarah Dwiastuti, Jiayun Luo, Jan Christian Blaise Cruz, Ming Shan Hee, Ikhlasul Akmal Hanif, M.Alif Al Hakim, Muhammad Rizky Sya’ban, Kun Kerdthaisong, Lester James Validad Miranda, Fajri Koto, Tirana Noor Fatyanosa, Alham Fikri Aji, Jostin Jerico Rosal, Jun Kevin, Robert Wijaya, Onno P. Kampman, Ruochen Zhang, Börje F. Karlsson, Peerat Limkonchotiwat

Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework (2026.acl-long)

Challenge:	Southeast Asia is underrepresented in vision-language research . SEA-VL is an open-source initiative dedicated to developing culturally relevant datasets for SEA languages.
Approach:	They propose to use crowdsourced, automated image crawling and synthetic image generation to develop culturally relevant datasets for SEA languages.
Outcome:	The proposed datasets capture SEA cultural nuances and contexts better than existing datasets.

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Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li, Tuan Dung Nguyen, Aditya Narayan Sankaran, Mai Huy Thong, Thanh Trung Nguyen, Mai Hong Son, Reza Farahbakhsh, Phi Le Nguyen, Noel Crespi

Investigating Context Faithfulness in Large Language Models: The Roles of Memory Strength and Evidence Style (2025.findings-acl)

Challenge:	Current methods map whole volumes to reports, ignoring the clinical workflow of analyzing localized Regions of Interest (RoIs) Current models exhibit suboptimal accuracy and are prone to significant hallucinations.
Approach:	They propose a framework that mimics the professional radiologist diagnostic workflow by employing graph-based relational modules to capture dependencies between RoI attributes.
Outcome:	The proposed framework surpasses existing models by 19.7% in BLEU and 4.7% in ROUGE-L while achieving a 45.8% improvement in clinical metrics.

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Yuepei Li, Kang Zhou, Qiao Qiao, Bach Nguyen, Qing Wang, Qi Li

RST Parsing from Scratch (2021.naacl-main)

Challenge:	Retrieval-augmented generation improves Large Language Models (LLMs) by integrating external information into the response generation process.
Approach:	They investigate the impact of memory strength and evidence presentation on LLMs’ receptiveness to external evidence by measuring the divergence in LLM responses to different paraphrases of the same question.
Outcome:	The proposed method improves Large Language Models (LLMs) by integrating external information into the response generation process.

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Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, Xiaoli Li

TrigReason: Trigger-Based Collaboration between Small and Large Reasoning Models (2026.findings-acl)

Challenge:	Fig. 1 shows a document level discourse parser that performs top-down end-to-end parsing without requiring segmentation .
Approach:	They propose a top-down end-to-end formulation of document level discourse parsing in the Rhetorical Structure Theory framework.
Outcome:	The proposed model outperforms existing methods in end-to-end parsing and parse with gold segmentation without handcrafted features.

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Yi Zhao, Yajuan Peng, Cam-Tu Nguyen, Zuchao Li, Xiaoliang Wang, Xiaoming Fu, Hai Zhao

Reasoning

Challenge:	Large Reasoning Models suffer from high inference latency due to autoregressive reasoning . SpecReason adopts a polling-based design that repeatedly invokes the LRM for verification at every step .
Approach:	They propose a trigger-based collaborative reasoning framework that delegates most reasoning to the SRM and activates LRM intervention only when necessary.
Outcome:	The proposed framework reduces latency and API cost by 73.3% under edge–cloud conditions.

Diversify Question Generation with Retrieval-Augmented Style Transfer (2023.emnlp-main)

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Qi Gou, Zehua Xia, Bowen Yu, Haiyang Yu, Fei Huang, Yongbin Li, Nguyen Cam-Tu

Text Generation

Challenge:	Existing question generation systems focus on the internal knowledge within the textual passage or the semantic word space for diverse content planning. Existing solutions focus on relying on the knowledge of the text and the semantic words, but have not considered the potential of external knowledge for expression diversity.
Approach:	They propose a framework for Retrieval-Augmented Style Transfer that utilizes the style of diverse templates for question generation.
Outcome:	The proposed framework outperforms baselines on diversity while being comparable in terms of consistency scores.

Byte Latent Transformer: Patches Scale Better Than Tokens (2025.acl-long)

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Artidoro Pagnoni, Ramakanth Pasunuru, Pedro Rodriguez, John Nguyen, Benjamin Muller, Margaret Li, Chunting Zhou, Lili Yu, Jason E Weston, Luke Zettlemoyer, Gargi Ghosh, Mike Lewis, Ari Holtzman, Srini Iyer

Green & Sustainable NLP

Challenge:	Existing large language models (LLMs) are trained on bytes, except for tokenization, which groups bytes into a static set of tokens.
Approach:	They propose a new byte-level LLM architecture that encodes bytes into dynamically sized patches, which serve as the primary units of computation.
Outcome:	The proposed architecture matches tokenization-based models with improvements in inference efficiency and robustness.

Better Alignment with Instruction Back-and-Forth Translation (2024.findings-emnlp)

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Thao Nguyen, Jeffrey Li, Sewoong Oh, Ludwig Schmidt, Jason Weston, Luke Zettlemoyer, Xian Li

MiMIC: Mitigating Visual Modality Collapse in Universal Multimodal Retrieval While Avoiding Semantic Misalignment (2026.findings-acl)

Challenge:	et al., 2023) proposes a method to improve instruction-tuning data . e.g., we generate synthetic instructions using the backtranslation approach .
Approach:	They propose a method to improve instruction-tuning data using web-based inputs . they generate synthetic instructions using the backtranslation approach and filter the generated data .
Outcome:	The proposed method improves the quality of instruction-tuning data based on preprocessed texts . it yields better AlpacaEval win rates than direct distillation .

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Li Juan, Chuanghao Ding, Xujie Zhang, Cam-Tu Nguyen

How effective is machine translation on low-resource code-switching? A case study comparing human and automatic metrics (2023.findings-acl)

Challenge:	Existing UMR methods can be broadly divided into two categories: early-fusion approaches, such as Marvel, which projects visual features into the language model space for integrating with text modality, and late-fusion methods, such UniVL-DR, which encode visual and textual inputs using separate encoders and obtain fused embeddings through addition.
Approach:	They propose to map different modalities into a shared embedding space for multi-modal retrieval.
Outcome:	Experiments on the WebQA+ and EVQA+ datasets show that MiMIC outperforms both early- and late-fusion approaches.

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Li Nguyen, Christopher Bryant, Oliver Mayeux, Zheng Yuan

Multilinguality

Challenge:	Specifically, we compare the performance of three MT systems in terms of their ability to translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively.
Approach:	They compare the performance of three machine translation systems in the context of machine translation (MT) they find that state-of-the-art neural translation systems achieve higher scores on automatic metrics when processing CSW input .
Outcome:	The proposed system can translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively.

Do Large Language Models Align with Core Mental Health Counseling Competencies? (2025.findings-naacl)

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Viet Cuong Nguyen, Mohammad Taher, Dongwan Hong, Vinicius Konkolics Possobom, Vibha Thirunellayi Gopalakrishnan, Ekta Raj, Zihang Li, Heather J. Soled, Michael L. Birnbaum, Srijan Kumar, Munmun De Choudhury

SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)

Challenge:	Large language models are promising for mental health, but their alignment with core counseling competencies remains underexplored.
Approach:	They propose a benchmark to evaluate 22 general-purpose and medical-finetuned LLMs across five key competencies.
Outcome:	The proposed model outperforms generalist models in Intake, Assessment & Diagnosis but struggles with core counseling attributes and professional practice & ethics.

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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing

Thermometer of Thoughts: Enhancing LLM’s Exploration via Attention Temperature Modulation (2026.acl-long)

Challenge:	Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages.
Approach:	They propose a series of language models that specifically focuses on Southeast Asian languages.
Outcome:	SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations .

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Zhiyuan Yu, Shijian Xiao, Cam-Tu Nguyen, Zhangyue Yin, Lekai Xing, Wenzhong Li, Sanglu Lu

Reasoning

Challenge:	Recent advances in the reasoning capabilities of large language models have enabled them to tackle complex tasks such as mathematics reasoning.
Approach:	They propose a method that modulates attention temperature dynamically to shift LLM's internal focus during reasoning, enabling a dynamic shift between exploratory and focused modes.
Outcome:	The proposed method improves Pass@10 by 6.78–14.20% and aggregation accuracy by 9.74% across 7 reasoning benchmarks.

Building a Personalized Dialogue System with Prompt-Tuning (2022.naacl-srw)

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Tomohito Kasahara, Daisuke Kawahara, Nguyen Tung, Shengzhe Li, Kenta Shinzato, Toshinori Sato

A Conditional Splitting Framework for Efficient Constituency Parsing (2021.acl-long)

Challenge:	We build a dialogue system that can respond based on a given character setting (persona) this method is not suitable because the more persona information is added, the longer the input text becomes.
Approach:	They propose to use prompt-tuning to build a dialogue system that responds based on a persona . they conduct automatic and manual evaluations on English and Japanese .
Outcome:	The proposed method can build a dialogue system with more natural responses with less computational resources than fine-tuning.

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Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, Xiaoli Li

GUI Agents: A Survey (2025.findings-acl)

Challenge:	Developing efficient and effective parsing solutions has always been a key focus in NLP.
Approach:	They propose a generic seq2seq parsing framework that casts constituency parsers into a series of conditional splitting decisions.
Outcome:	The proposed framework outperforms state-of-the-art (SoTA) methods in discourse parsing . it is based on a syntactic and discourse parsed model and is linear in number of nodes .

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Dang Nguyen, Jian Chen, Yu Wang, Gang Wu, Namyong Park, Zhengmian Hu, Hanjia Lyu, Junda Wu, Ryan Aponte, Yu Xia, Xintong Li, Jing Shi, Hongjie Chen, Viet Dac Lai, Zhouhang Xie, Sungchul Kim, Ruiyi Zhang, Tong Yu, Mehrab Tanjim, Nesreen K. Ahmed, Puneet Mathur, Seunghyun Yoon, Lina Yao, Branislav Kveton, Jihyung Kil, Thien Huu Nguyen, Trung Bui, Tianyi Zhou, Ryan A. Rossi, Franck Dernoncourt

Challenge:	Large Foundation Models (LFMs) have transformed the landscape of AI research and day-to-day life.
Approach:	They propose a framework that delineates GUI agents' perception, reasoning, planning, and acting capabilities.
Outcome:	The proposed framework delineates their perception, reasoning, planning, and acting capabilities.

Discovering Language Model Behaviors with Model-Written Evaluations (2023.findings-acl)

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Ethan Perez, Sam Ringer, Kamile Lukosiute, Karina Nguyen, Edwin Chen, Scott Heiner, Craig Pettit, Catherine Olsson, Sandipan Kundu, Saurav Kadavath, Andy Jones, Anna Chen, Benjamin Mann, Brian Israel, Bryan Seethor, Cameron McKinnon, Christopher Olah, Da Yan, Daniela Amodei, Dario Amodei, Dawn Drain, Dustin Li, Eli Tran-Johnson, Guro Khundadze, Jackson Kernion, James Landis, Jamie Kerr, Jared Mueller, Jeeyoon Hyun, Joshua Landau, Kamal Ndousse, Landon Goldberg, Liane Lovitt, Martin Lucas, Michael Sellitto, Miranda Zhang, Neerav Kingsland, Nelson Elhage, Nicholas Joseph, Noemi Mercado, Nova DasSarma, Oliver Rausch, Robin Larson, Sam McCandlish, Scott Johnston, Shauna Kravec, Sheer El Showk, Tamera Lanham, Timothy Telleen-Lawton, Tom Brown, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Jack Clark, Samuel R. Bowman, Amanda Askell, Roger Grosse, Danny Hernandez, Deep Ganguli, Evan Hubinger, Nicholas Schiefer, Jared Kaplan

Mastering the Craft of Data Synthesis for CodeLLMs (2025.naacl-long)

Challenge:	Prior work creates evaluations with crowdwork or existing data sources, which are not always available.
Approach:	They generate evaluations automatically with language models (LMs) using crowdwork or existing data sources to find out how they behave .
Outcome:	The results show that large LMs repeat back a dialog user’s preferred answer and express greater desire to pursue concerning goals like resource acquisition and goal preservation.

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Meng Chen, Philip Arthur, Qianyu Feng, Cong Duy Vu Hoang, Yu-Heng Hong, Mahdi Kazemi Moghaddam, Omid Nezami, Duc Thien Nguyen, Gioacchino Tangari, Duy Vu, Thanh Vu, Mark Johnson, Krishnaram Kenthapadi, Don Dharmasiri, Long Duong, Yuan-Fang Li

Challenge:	Large language models (LLMs) have shown impressive performance in code understanding and generation.
Approach:	They propose a systematic review of large language models and their taxonomy and propose specialized LLMs for code-related tasks.
Outcome:	The proposed models have shown to be highly effective in coding tasks.

Efficient Constituency Parsing by Pointing (2020.acl-main)

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Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, Xiaoli Li