Papers by Trang Vu
Systematic Assessment of Factual Knowledge in Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing question-answering benchmarks for large language models have limitations regarding factual knowledge coverage, as they focus on generic domains and overlap with pretraining data. |
| Approach: | They propose a framework to assess the factual knowledge of large language models by leveraging knowledge graphs. |
| Outcome: | The proposed framework generates questions and expected answers from the facts stored in a given knowledge graph and evaluates them with KGs in generic and specific domains. |
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. |
Continual Learning of Large Language Models (2025.emnlp-tutorials)
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| Challenge: | This tutorial explores the challenges of continual learning in large language models . participants will learn strategies to mitigate forgetting and manage data and evaluation pipelines . |
| Approach: | This tutorial offers a comprehensive exploration of continual learning in the context of large language models. |
| Outcome: | This tutorial explores the challenges of continual learning in large language models . participants will learn how to manage data and evaluation pipelines and adapt responsibly . |