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Tingchen Fu

Tingchen Fu has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning; often with Yafu Li, Chen Xu, Deng Cai.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 5 · 21 works · 565 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 506 times in our library · ⭐ Paper Stars ranking: #2503

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

🎓Renmin University of ChinaBeijing · China2022–2026 · 17 paperscurrent🔬Shanghai Artificial Intelligence LaboratoryShanghai · China2026 · 1 papercurrent

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 2Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Large language models · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models (2023 · 977 citations)
    Large language models often write fluent text that is wrong, and this survey gathers recent work on how to spot, explain and reduce such hallucinations.
  2. Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models (2026 · 12 citations)
    Large reasoning models (LRMs) excel at hard math but often ignore simple user directives. Existing instruction-following benchmarks such as IFEval and FollowBench rely on easy queries that trigger little reasoning, so they do not reflect how LRMs are actually used. To address this, the authors built MathIF, a…

 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 506 times (of the 149,439 papers whose bibliographies we read); 96 citing papers with a page here →.

Updated

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