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.
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
Affiliations
Fields, topics and concepts
📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArtificial Intelligence · 4 worksArts and Humanities · 3 worksLanguage and Linguistics · 3 worksSocial Sciences · 1 workInformation Systems · 1 workSociology and Political Science · 1 work
🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1 workMultilingual & cross-lingual NLP · 1 work
Works by year
2020s
Papers
- 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. - 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… - 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. - 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
- Timothy Baldwin ×3
- Alexander Panchenko ×2
- Artem Vazhentsev ×2
- Ekaterina Fadeeva ×2
- Preslav Nakov ×2
- Abdelrahman Sadallah
- Artem Shelmanov
- Giovanni Puccetti
- Gleb Kuzmin
- Ivan Lazichny
- Jey Han Lau
- Jiahui Geng
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2025
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
- Transactions of the Association for Computational Linguistics (TACL): Transactions of the Association for Computational Linguistics (TACL) 2025
Cited by
Updated
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