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Qing Yang

Qing Yang has 4 papers in our library, in 2023–2025, most of them on Language models & pretraining, Question answering, Bias, fairness & safety; often with Xuanyu Zhang, Dongliang Xu, Deyi Xiong.

4 papers in our library · 2023–2025

OpenAlex · ORCID · Wikidata · h-index 53 · 810 works · 13,626 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 172 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5904

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. 151 more institutions on single bylines are not listed.

 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. XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters (2023 · 125 citations)
    The authors address the absence of open-source, very large chat models built for Chinese, and specifically for Chinese finance.
  2. Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling (2025 · 22 citations)
    LLM inference is slow because autoregressive decoding emits one token per step, and each step is dominated by memory transfer of billions of weights.
  3. Self-QA: Unsupervised Knowledge Guided Language Model Alignment (2023 · 19 citations)
    Instruction tuning usually depends on question–answer pairs written by people. Such data is costly to produce, and it is hard to keep it accurate, varied and focused on a domain. Bootstrapping methods like Self-Instruct reduce that cost, but they still begin from hand-made seed prompts and offer little control over…
  4. Automated Progressive Red Teaming (2025 · 10 citations)
    Human red teaming of LLMs works but is slow and costly, and most automated attackers either stay frozen or never learn from the target model's feedback.

All works

5 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. Reinforcement Learning for Reasoning in Large Language Models with One Training Example ↗arXiv:2504.20571 · no summary yetYiping Wang et al.2025 · 87 citations
  2. Self-QA: Unsupervised Knowledge Guided Language Model AlignmentarXiv:2305.11952Xuanyu Zhang, Qing Yang2023 · 19 citations

 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 172 times (of the 149,439 papers whose bibliographies we read); 35 citing papers with a page here →.

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