Papers by Chau Nguyen
SConE: Simplified Cone Embeddings with Symbolic Operators for Complex Logical Queries (2023.findings-acl)
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| Challenge: | Current geometric-based methods depend on the neural approach to model FOL operators . empirical evidence for explainability is challenging . |
| Approach: | They propose to model conjunction operators using a symbolic modeling approach . they propose to emphasize the essential role of relation projection operator . |
| Outcome: | The proposed method improves answering complex logical queries over previous models. |
CovRelex-SE: Adding Semantic Information for Relation Search via Sequence Embedding (2023.eacl-demo)
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| Challenge: | COVID-19 has affected all aspects of human life, causing problems related to acronyms, synonyms, and rare keywords. |
| Approach: | They propose a hybrid relation retrieval system based on embeddings to provide high-quality search results. |
| Outcome: | The proposed system can be accessed through the following URL: http://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/. |
CovRelex: A COVID-19 Retrieval System with Relation Extraction (2021.eacl-demos)
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| Challenge: | Existing challenges to making the system more practical include dealing with newly created and unknown data, and solving the performance gap when utilizing present data. |
| Approach: | They propose a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers. |
| Outcome: | The proposed system can be accessed via https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex/. |
VMLU Benchmarks: A comprehensive benchmark toolkit for Vietnamese LLMs (2025.acl-long)
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Cuc Thi Bui, Nguyen Truong Son, Truong Van Trang, Lam Viet Phung, Pham Nhut Huy, Hoang Anh Le, Quoc Huu Van, Phong Nguyen-Thuan Do, Van Le Tran Truc, Duc Thanh Chau, Le-Minh Nguyen
| 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. |