Papers by Trang Nguyen

3 papers
Causal Reasoning through Two Cognition Layers for Improving Generalization in Visual Question Answering (2023.emnlp-main)

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Challenge: Existing attempts to generalize VQA focus on unimodal aspects, overlooking enhancements in multimodal aspects.
Approach: They propose to decompose the responsibility of each stage into distinct experts and a cognition-enabled component (CC) they prioritize answer predictions governed by pathways involving both CCs while disregarding answers produced by either CC.
Outcome: The proposed model improves multimodal predictions by emphasizing causal reasoning factors.
O_O-VC: Synthetic Data-Driven One-to-One Alignment for Any-to-Any Voice Conversion (2025.findings-emnlp)

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Challenge: Traditional voice conversion methods attempt to separate speaker identity and linguistic information into distinct representations, but this method often leads to information loss during training.
Approach: They propose a method that leverages synthetic speech data generated by a pretrained model . synthetic data pairs that share the same linguistic content are used as input-output pairs .
Outcome: The proposed method outperforms state-of-the-art methods in speaker-to-voice conversions.
VMLU Benchmarks: A comprehensive benchmark toolkit for Vietnamese LLMs (2025.acl-long)

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Challenge: The evolution of Large Language Models (LLMs) has underscored the need for benchmarks designed for various languages and cultural contexts.
Approach: They propose to use Vietnamese multitask language understanding (VMLU) benchmarks to assess different capabilities of LLMs, including general knowledge, reading comprehension, reasoning, and conversational skills.
Outcome: The VMLU Benchmarks assess LLMs' general knowledge, reading comprehension, reasoning, and conversational skills.

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