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Rui Xing

Rui Xing has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP; often with Timothy Baldwin, Alexander Panchenko, Artem Vazhentsev.

4 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 3 · 17 works · 48 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 70 times in our library · ⭐ Paper Stars ranking: #7075

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

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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph (2025 · 67 citations)
    Uncertainty quantification (UQ) can help flag hallucinated or low-quality outputs of large language models. Earlier work on UQ for LLMs used incompatible methods and evaluation setups, so findings were hard to compare.
  2. Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models (2025 · 10 citations)
    LLMs score each new token assuming everything generated so far is correct. If an early token is wrong, the tokens that follow can still look highly confident, so uncertainty estimates built on raw probabilities understate the risk. The authors argue that what matters is a token's unconditional chance of being…
  3. Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI (2026 · 5 citations)
    Earlier work suggested that people can barely tell LLM output from human writing. Most of that evidence came from English text, GPT-3.5-turbo, and small samples. This paper tests whether the claim holds for newer models, other languages and expert readers, aiming to estimate the ceiling of human detection ability.
  4. COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations (2026 · 2 citations)
    Platforms such as X, Meta and TikTok increasingly rely on crowd-written notes rather than professional fact-checkers to explain why a post may mislead.

 Co-authors here

12 of 22 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 70 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

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