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Tiancheng Hu

Tiancheng Hu has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining, Machine translation, Low-resource languages; often with Nigel Collier, Caiqi Zhang, Chengzu Li.

3 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 7 · 22 works · 242 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 93 times in our library · ⭐ Paper Stars ranking: #7281

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

🎓University of CambridgeCambridge · United Kingdom2023–2026 · 37 papersEducationcurrent🎓ETH ZurichZurich · Switzerland2021–2023 · 8 papers🎓Ludwig-Maximilians-Universität MünchenMunich · Germany2026 · 3 papers🎓University of HelsinkiHelsinki · Finland2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksLow-resource languages · 1 workMachine translation · 1 work

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Quantifying the Persona Effect in LLM Simulations (2024 · 116 citations)
    The paper asks how much demographic, social, attitudinal and behavioral "persona" information actually helps LLMs reproduce how different people judge subjective content.
  2. Scaling Low-Resource MT via Synthetic Data Generation with LLMs (2025 · 15 citations)
    Most of the world's languages lack enough human-translated parallel text to train strong machine translation systems.
  3. Confidence Estimation for LLMs in Multi-turn Interactions (2026 · 7 citations)
    Most work on LLM confidence estimation looks at single questions answered once. This paper studies a different case: multi-turn dialogue, where useful information arrives over several turns. The authors argue that a good confidence signal in this setting must do two things. It should be calibrated at each turn, and…

 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 93 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

Updated

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