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Thai Hoang

Thai Hoang has 2 papers in our library, in 2025, most of them on Machine translation; often with Jianguo Zhang, Juan Carlos Niebles, Ming Zhu.

2 papers in our library · 2025

OpenAlex · h-index 2 · 7 works · 22 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 83 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6770

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Ho Chi Minh City University of TechnologyHo Chi Minh City · Vietnam2026 · 2 paperscurrent🎓Vietnam National University Ho Chi Minh CityHo Chi Minh City · Vietnam2026 · 2 paperscurrent🏢SalesforceSan Francisco · United States2024–2025 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 1Research subjects and languages the work is about.Machine translation · 1 work

 Works by year

2020s

20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025 · 68 citations)
    Open-source LLMs lag behind proprietary systems on agent tasks such as tool use. A major cause is that public agent datasets are scarce, inconsistent in format, homogeneous in content, and often noisy, with problems like hallucinated actions or repeated turns. The authors (Salesforce AI) release xLAM, a family of…
  2. APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay (2025 · 36 citations)
    Building capable multi-turn AI agents requires training data that captures realistic exchanges between humans and agents.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. xLAM: A Family of Large Action Models to Empower AI Agent SystemsConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Jianguo Zhang, Tian Lan, Ming Zhu, et al.2025 · 68 citations
  2. APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human InterplayConference on Neural Information Processing Systems (NeurIPS)Akshara Prabhakar, Zuxin Liu, Ming Zhu, et al.2025 · 36 citations
  3. Not-NUTs at WNUT-2020 Task 2: A BERT-based System in Identifying Informative COVID-19 English Tweets ↗Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020) · no summary yetThai Hoang, Phuong Vu2020

 Co-authors here

12 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 83 times (of the 149,439 papers whose bibliographies we read); 23 citing papers with a page here →.

Updated

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