Papers by Hieu Tran
README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLP (2024.findings-emnlp)
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Zonghai Yao, Nandyala Siddharth Kantu, Guanghao Wei, Hieu Tran, Zhangqi Duan, Sunjae Kwon, Zhichao Yang, Hong Yu
| Challenge: | a new task is to generate lay definitions of medical terms in EHRs that are difficult to understand for patients. |
| Approach: | They propose a task of automatically generating lay definitions to simplify medical terms into patient-friendly lay language. |
| Outcome: | The proposed model can match or surpass state-of-the-art closed-source large language models like ChatGPT with high-quality data. |
Exploiting Tree Structure for Credit Assignment in Reinforcement Learning with Large Language Models (2026.findings-acl)
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| Challenge: | Reinforcement learning has shown strong promise for strengthening reasoning ability of large language models, but sparse, delayed rewards make token-level credit assignment a central challenge. |
| Approach: | They propose a critic-free algorithm that rewards tokens that change the solution. |
| Outcome: | The proposed algorithm improves on in-distribution benchmarks and out-of-disttribution settings. |
PSED: A Dataset for Selecting Emphasis in Presentation Slides (2021.findings-acl)
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Amirreza Shirani, Giai Tran, Hieu Trinh, Franck Dernoncourt, Nedim Lipka, Jose Echevarria, Thamar Solorio, Paul Asente
| Challenge: | null |
| Approach: | null |
| Outcome: | null |
Enriching Biomedical Knowledge for Low-resource Language Through Large-scale Translation (2023.eacl-main)
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| Challenge: | Biomedical data and benchmarks are highly valuable but limited in low-resource languages such as English. |
| Approach: | They propose a translation model in Vietnamese that trains a pretrained Encoder-Decoder Transformer model on 20 million translated abstracts. |
| Outcome: | The proposed model can translate and produce both pretrained and supervised biomedical data in two biomedically important domains. |
VietMix: A Naturally-Occurring Parallel Corpus and Augmentation Framework for Vietnamese-English Code-Mixed Machine Translation (2026.eacl-long)
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| Challenge: | Existing approaches to machine translation (MT) systems degrade when faced with code-mixed text. |
| Approach: | They propose a system that can augment Vietnamese-English code-mixed text with iterative fine-tuning and targeted filtering. |
| Outcome: | The proposed framework outperforms strong back-translation baselines and improves zero-shot models by up to +11.9 points. |
ViT5: Pretrained Text-to-Text Transformer for Vietnamese Language Generation (2022.naacl-srw)
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| Challenge: | Existing models for the English language have been used to train on large corpus of high-quality texts. |
| Approach: | They present a pretrained Transformer-based encoder-decoder model for the Vietnamese language . they benchmark ViT5 on two downstream text generation tasks . |
| Outcome: | The proposed model outperforms existing models on Vietnamese Abstractive Summarization and Named Entity Recognition tasks. |
Class based Influence Functions for Error Detection (2023.acl-short)
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Thang Nguyen-Duc, Hoang Thanh-Tung, Quan Hung Tran, Dang Huu-Tien, Hieu Nguyen, Anh T. V. Dau, Nghi Bui
| Challenge: | Influence functions (IFs) are powerful tools for detecting anomalous examples in large scale datasets. |
| Approach: | They propose a method to explain the instability of IFs by leveraging class information to improve the stability of ifs. |
| Outcome: | The proposed method improves performance and stability while incurring no additional computational cost. |
RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models (2025.acl-long)
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Hieu Tran, Zonghai Yao, Zhichao Yang, Junda Wang, Yifan Zhang, Shuo Han, null Feiyun Ouyang, Hong Yu
| Challenge: | Existing work aims to improve reasoning accuracy and factual integrity across large language models for knowledge-intensive tasks such as medical and commonsense reasoning. |
| Approach: | They propose a versatile extension to the mutual reasoning framework (rStar) that enhances reasoning accuracy and factual integrity across large language models. |
| Outcome: | The proposed extension to the mutual reasoning framework improves reasoning accuracy and factual integrity across large language models for complex, knowledge-intensive tasks. |
LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models (2025.emnlp-industry)
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| Challenge: | Large language models (LLMs) struggle with factual accuracy in knowledge-intensive domains like healthcare. |
| Approach: | They propose a framework for improving LLM factuality in medical question answering . RAFE, Fact-Check-then-RAG and Learning from Fact Check are components . |
| Outcome: | Experimental results show that LEAF outperforms Factcheck-GPT in detecting inaccuracies and corrects errors without labeling . the framework provides a scalable solution for industrial applications requiring high factuality scores. |
Chatbot To Help Patients Understand Their Health (2025.findings-emnlp)
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Won Seok Jang, Hieu Tran, Manav Shaileshkumar Mistry, Sai Kiran Gandluri, Yifan Zhang, Sharmin Sultana, Sunjae Kwon, Yuan Zhang, Zonghai Yao, Hong Yu
| Challenge: | NoteAid-Chatbot is a conversational AI designed to help patients better understand their health . |
| Approach: | They propose a new learning paradigm that leverages a multi-agent large language model and reinforcement learning framework without relying on costly human-generated training data. |
| Outcome: | The proposed framework surpasses non-expert human training methods. |