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Hexiang Tan

Hexiang Tan has 3 papers in our library, in 2024–2026, most of them on Language models & pretraining; often with Fei Sun, Xueqi Cheng, Yuanzhuo Wang.

3 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 5 · 15 works · 212 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 32 times in our library · ⭐ Paper Stars ranking: #7792

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

🔬Institute of Computing TechnologyBeijing · China2023–2026 · 11 papersEmployment · PHD, niversity of Chinese Academy of Sciencescurrent🎓University of Chinese Academy of SciencesBeijing · China2023–2026 · 10 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2023–2026 · 5 paperscurrent🎓Nanjing University of Posts and TelecommunicationsNanjing · China2020 · 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

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

 Works by year

2020s

20261 paper20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Blinded by Generated Contexts: How Language Models Merge Generated and Retrieved Contexts When Knowledge Conflicts? (2024 · 37 citations)
    Hybrid question-answering systems give an LLM two kinds of context: passages it wrote itself and passages retrieved from an external corpus such as Wikipedia.
  2. Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs (2025 · 12 citations)
    LLM error detectors often treat agreement across sampled answers as a sign of correctness. This paper identifies a blind spot in that assumption: self-consistent errors , where a model gives the same wrong answer, up to semantic equivalence, in its greedy output and in all stochastic samples. The authors define these…
  3. BaseCal: Unsupervised Confidence Calibration via Base Model Signals (2026 · 1 citation)
    Post-trained LLMs (PoLLMs), such as instruction-tuned or RLHF-aligned models, tend to be overconfident: they assign high probability even to wrong answers, which limits the usefulness of confidence for detecting hallucinations.

 Co-authors here

10 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 32 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

Updated

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