70 citations · 87 across the 14 of their papers we have counts for
15 papers
MCBench: A Multicontext Safety Assessment Benchmark for Omni Large Language Models
Manh Luong, Tamas Abraham, Junae Kim +6
Existing multimodal safety benchmarks focus solely on visual inputs and cannot assess Omni Large Language Models (LLMs) that process vision, audio, and text. We introduce MCBench,…
TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment
Thi-Nhung Nguyen, Linhao Luo, Rollin Omari +3
Personalized large language models adapt responses to users' preferences and social attributes, but can introduce substantial universal truth inconsistencies across social groups,…
MATO: Multi-objective Personalized Alignment with Test-time Optimization for Large Language Models
Linhao Luo, Thuy-Trang Vu, Van-Anh Nguyen +3
Aligning large language models (LLMs) with diverse and multifaceted user preferences is a fundamental challenge in personalized AI systems. Existing multi-objective alignment metho…
LiveCultureBench: a Multi-Agent, Multi-Cultural Benchmark for Large Language Models in Dynamic Social Simulations
Viet-Thanh Pham, Lizhen Qu, Thuy-Trang Vu +2
Large language models (LLMs) are increasingly deployed as autonomous agents, yet evaluations focus primarily on task success rather than cultural appropriateness or evaluator relia…
The Social Cost of Intelligence: Emergence, Propagation, and Amplification of Stereotypical Bias in Multi-Agent Systems
Thi-Nhung Nguyen, Linhao Luo, Amardeep Kaur +5
Bias in large language models (LLMs) remains a persistent challenge, often leading to stereotyping and unfair treatment across social groups. While prior work has mainly focused on…
G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge
Linhao Luo, Zicheng Zhao, Junnan Liu +9
Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge. Retrieval-augmented generation (RAG) mitigates this by inc…
Improving Table Understanding with LLMs and Entity-Oriented Search
Thi-Nhung Nguyen, Hoang Ngo, Dinh Phung +2
Our work addresses the challenges of understanding tables. Existing methods often struggle with the unpredictable nature of table content, leading to a reliance on preprocessing an…
Planning for Success: Exploring LLM Long-term Planning Capabilities in Table Understanding
Thi-Nhung Nguyen, Hoang Ngo, Dinh Phung +2
Table understanding is key to addressing challenging downstream tasks such as table-based question answering and fact verification. Recent works have focused on leveraging Chain-of…
Unbiased Sliced Wasserstein Kernels for High-Quality Audio Captioning
Manh Luong, Khai Nguyen, Dinh Phung +2
Audio captioning systems face a fundamental challenge: teacher-forcing training creates exposure bias that leads to caption degeneration during inference. While contrastive methods…
GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation
Linhao Luo, Zicheng Zhao, Gholamreza Haffari +3
Retrieval-augmented generation (RAG) has proven effective in integrating knowledge into large language models (LLMs). However, conventional RAGs struggle to capture complex relatio…
Revisiting Deep Audio-Text Retrieval Through the Lens of Transportation
Manh Luong, Khai Nguyen, Nhat Ho +3
The Learning-to-match (LTM) framework proves to be an effective inverse optimal transport approach for learning the underlying ground metric between two sources of data, facilitati…
PhoGPT: Generative Pre-training for Vietnamese
Dat Quoc Nguyen, Linh The Nguyen, Chi Tran +3
We open-source a state-of-the-art 4B-parameter generative model series for Vietnamese, which includes the base pre-trained monolingual model PhoGPT-4B and its chat variant, PhoGPT-…
Exploiting Domain-Specific Features to Enhance Domain Generalization
Manh-Ha Bui, Toan Tran, Anh Tuan Tran +1
Domain Generalization (DG) aims to train a model, from multiple observed source domains, in order to perform well on unseen target domains. To obtain the generalization capability,…
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection
Thuy-Trang Vu, Xuanli He, Dinh Phung +1
This paper considers the unsupervised domain adaptation problem for neural machine translation (NMT), where we assume the access to only monolingual text in either the source or ta…
Multi-Label Image Classification with Contrastive Learning
Son D. Dao, Ethan Zhao, Dinh Phung +1
Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains. The success of constrastive…