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Bingzhe Wu

Bingzhe Wu has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Bias, fairness & safety; often with Lemao Liu, Zhirui Zhang.

2 papers in our library · 2023–2026

OpenAlex · ORCID · Wikidata · h-index 20 · 94 works · 1,256 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 119 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #7450

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

🎓Shenzhen UniversityShenzhen · China2025–2026 · 11 paperscurrent🏢TencentShenzhen · China2021–2025 · 42 papers🎓Peking UniversityBeijing · China2017–2024 · 15 papers🎓Chinese University of Hong Kong, ShenzhenShenzhen · China2022–2025 · 4 papers🏢Ant GroupHangzhou · China2020–2021 · 4 papers🎓Chinese University of Hong KongHong Kong · China2023–2024 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksBias, fairness & safety · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Fairness-guided Few-shot Prompting for Large Language Models (2023 · 64 citations)
    In-context learning in large language models is unstable: accuracy can change sharply depending on which few-shot demonstrations are chosen and how they are ordered.
  2. CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMs (2026)
    LLM-based content moderation often fails when harm is implicit, such as discrimination expressed indirectly or through metaphor, or when moderation criteria are vague.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. LLM Inference Unveiled: Survey and Roofline Model Insights ↗arXiv 2024 · no summary yetZhihang Yuan et al.2024 · 91 citations
  2. Fairness-guided Few-shot Prompting for Large Language ModelsarXiv:2303.13217Huan Ma, Changqing Zhang, Yatao Bian, et al.2023 · 64 citations
  3. CHAIRO: Contextual Hierarchical Analogical Induction and Reasoning Optimization for LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Haotian Lu, Yuchen Mou, Bingzhe Wu2026
  4. CARO: Chain-of-Analogy Reasoning Optimization for Robust Content Moderation ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetBingzhe Wu, Haotian Lu, Yuchen Mou2026

 Co-authors here

2 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 119 times (of the 149,439 papers whose bibliographies we read); 14 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/bingzhe-wu/