Papers by Duy Nguyen
Structural and Functional Decomposition for Personality Image Captioning in a Communication Game (2020.findings-emnlp)
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| Challenge: | Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait. |
| Approach: | They propose to use a communication game between a speaker and a listener to generate captions for PIC. |
| Outcome: | The proposed model achieves state-of-the-art performance for personal image captioning (PIC) the proposed model is based on a communication game between a speaker and a listener . |
What Makes a Good Natural Language Prompt? (2025.acl-long)
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| Challenge: | Existing studies on prompt quality show imbalanced support across models and tasks, and research gaps. |
| Approach: | They propose a property- and human-centric framework for evaluating prompt quality . they propose comparing prompt quality to other factors such as adverbs and apverbs . |
| Outcome: | The proposed framework reveals imbalanced support across models and tasks and substantial research gaps. |
Multi-Attribute Steering of Language Models via Targeted Intervention (2025.acl-long)
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| Challenge: | Existing approaches for steering large language models fail to scale to multi-attribute settings with conflicts, such as enhancing helpfulness while also reducing toxicity. |
| Approach: | They propose a steering framework for selective token-level intervention across multiple attributes that enforcing sparsity and orthogonality among vectors for different attributes. |
| Outcome: | The proposed framework outperforms existing ITI and parameter-efficient fine-tuning approaches across question answering tasks and generative tasks. |
Fooling the Textual Fooler via Randomizing Latent Representations (2024.findings-acl)
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| Challenge: | Several adversarial attacks can compromise the model without accessing the model architecture or model parameters (i.e., a blackbox setting) Several studies have revealed that deep NLP models are vulnerable to adversarials that slightly perturb the input to cause the models to misbehave. |
| Approach: | They propose a lightweight and attack-agnostic defense that perplexes the process of generating an adversarial example in query-based black-box attacks. |
| Outcome: | The proposed defense is lightweight and attack-agnostic and does not necessitate additional computational overhead during training nor does it rely on assumptions about the potential adversarial perturbation set while having a negligible impact on the model’s accuracy. |
Unsupervised Domain Adaptation for Event Detection using Domain-specific Adapters (2021.findings-acl)
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| Challenge: | Existing approaches to ED are limited due to complexity of textual data and domain shift problem. |
| Approach: | They propose to use domain adapter-based Adaptation framework to improve event detection across domains. |
| Outcome: | The proposed framework significantly boosts the performance on target domains. |
Distributional Surgery for Language Model Activations (2025.findings-emnlp)
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| Challenge: | Language models can produce undesirable outputs including harmful or toxic outputs. |
| Approach: | They propose a method to detect undesirable content using activations . they propose layerwise distributional steering policies that transform the attention heads . |
| Outcome: | The proposed method outperforms baselines in reducing undesirable output generation. |
Exploiting Document Structures and Cluster Consistencies for Event Coreference Resolution (2021.acl-long)
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| Challenge: | Existing deep learning models for event coreference resolution are limited in that they cannot exploit important interactions between relevant objects for ECR. |
| Approach: | They propose a deep learning model that groups coreferent event mentions into the same clusters . they use document structures to capture relevant objects for ECR . |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets. |
Sentiment Reasoning for Healthcare (2025.acl-industry)
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| Challenge: | Sentiment Reasoning is an auxiliary task in sentiment analysis where the model predicts both the sentiment label and generates the rationale behind it based on the input transcript. |
| Approach: | They propose a task - Sentiment Reasoning - for both speech and textmodalities and propose 'multimodal multitask framework' . they propose to use a model that generates the rationale behind each predicted label and provides a rationale for model prediction with quality semantically comparable to humans. |
| Outcome: | The proposed task improves model transparency by providing rationale for model prediction with quality semantically comparable to humans while improving model’s classification performance. |
GrAInS: Gradient-based Attribution for Inference-Time Steering of LLMs and VLMs (2026.acl-long)
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| Challenge: | Existing methods for fine-tuning large language models often ignore token-level causal influence and underutilize model logits. |
| Approach: | They propose a novel approach that uses a gradient-based approach to identify influential tokens and construct directional steering vectors based on their contribution to preferred over dispreferred outputs. |
| Outcome: | The proposed approach outperforms fine-tuning and prior steering methods on both LLM and VLM tasks without degrading fluency or general capabilities. |
Mastering the Craft of Data Synthesis for CodeLLMs (2025.naacl-long)
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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. |