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Yuxia Wang

Yuxia Wang has 5 papers in our library, in 2022–2026, most of them on Bias, fairness & safety, Language models & pretraining, Reasoning; often with Preslav Nakov, Haonan Li, Timothy Baldwin.

5 papers in our library · 2022–2026

OpenAlex · ORCID · h-index 23 · 181 works · 1,490 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 426 citations in our library · 6 more in our library without a page yet · ⭐ Paper Star rank #4745

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. 48 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Do-Not-Answer: Evaluating Safeguards in LLMs (2024 · 91 citations)
    Open-source LLMs often ship without the safety layers that commercial systems have. Existing harmful-prompt collections tend to be small, proprietary, or unlabeled. This paper offers a cheap, public way to test whether a model refuses requests it should not fulfil.
  2. Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs (2023 · 76 citations)
    Open-source LLMs often ship without strong safety mechanisms, and earlier harmful-prompt collections were either private, narrow in scope, or lacked labeled model responses.
  3. Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference (2022 · 15 citations)
    NLI items often have no single right answer: when many people label the same premise–hypothesis pair, their votes split.
  4. Harm or Humor: A Multimodal, Multilingual Benchmark for Overt and Covert Harmful Humor (2026)
    The paper argues that detecting harmful humor is a safety problem that existing toxicity benchmarks cover poorly.
  5. MuDRiC: Multi-Dialect Reasoning for Arabic Commonsense Validation (2025)
    Arabic commonsense benchmarks have mostly covered Modern Standard Arabic (MSA), while everyday communication happens in regional dialects. MSA-trained models transfer poorly to them. The authors build MuDRiC, which they describe as the first multi-dialect Arabic commonsense validation resource.

All works

11 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them.

  1. M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection (original ↗)Yuxia Wang et al.2023 · arXiv:2305.14902 · 106 citations · no summary yet
  2. A Survey of Confidence Estimation and Calibration in Large Language Models (original ↗)Jiahui Geng et al.2023 · arXiv:2311.08298 · 96 citations · no summary yet
  3. Factuality of Large Language Models: A Survey (original ↗)Yuxia Wang et al.2024 · Proceedings of the 2024 Conference on Empirical… · 76 citations · no summary yet
  4. Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMsYuxia Wang, Haonan Li, Xudong Han, et al.arXiv:2308.13387 · 76 citations
  5. Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression (original ↗)Yuxia Wang et al.2022 · Transactions of the Association for… · 48 citations · no summary yet
  6. Arabic Dataset for LLM Safeguard Evaluation (original ↗)Yasser Ashraf et al.2025 · Proceedings of the 2025 Conference of the… · 22 citations · no summary yet
  7. FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning (original ↗)Zhuohan Xie et al.2026 · Proceedings of the 64th Annual Meeting of the… · 10 citations · no summary yet
  8. Harm or Humor: A Multimodal, Multilingual Benchmark for Overt and Covert Harmful HumorAhmed Sharshar, Hosam Elgendy, Saad El Dine Ahmed, et al.arXiv:2603.17759
  9. MuDRiC: Multi-Dialect Reasoning for Arabic Commonsense ValidationKareem Elozeiri, Mervat Abassy, Preslav Nakov, et al.arXiv:2508.13130

 Co-authors here

8 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 426 times (of the 141,840 papers whose bibliographies we read); 29 citing papers with a page here →.

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