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20212026
most citedExploiting Domain-Specific Features to Enhance Domain Generalization

70 citations · 86 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

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,…

cs.CL2026

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2023

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-…

cs.CL2021

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…