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Kuan Wang

Kuan Wang has 2 papers in our library, in 2023–2025, most of them on Language models & pretraining, Question answering, Reasoning; often with Baolin Peng, Chao Zhang, Hao Cheng.

2 papers in our library · 2023–2025

OpenAlex · ORCID · h-index 14 · 70 works · 1,879 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 298 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5799

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

🎓Georgia Institute of TechnologyAtlanta · United States2021–2026 · 9 papers🎓Massachusetts Institute of TechnologyCambridge · United States2019–2020 · 8 papers

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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksQuestion answering · 1 workReasoning · 1 work

 Works by year

2020s

20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. ToolQA: A Dataset for LLM Question Answering with External Tools (2023 · 182 citations)
    LLMs often hallucinate and struggle with arithmetic, and connecting them to external tools can help. Existing benchmarks, however, cannot tell whether a model actually used a tool or simply recalled facts it had seen during pre-training. ToolQA addresses this by building questions that can be solved only by querying…
  2. Reinforcement Learning for Reasoning in Large Language Models with One Training Example (2025 · 87 citations)
    The paper asks how small a training set can be for reinforcement learning with verifiable reward (RLVR). RLVR is the outcome-rewarded RL now used to strengthen math reasoning in LLMs. The authors draw a random sample of 1,209 problems from the DeepScaleR dataset (DSR-sub). They rank each problem by how much its…

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. HAQ: Hardware-Aware Automated Quantization With Mixed Precision ↗IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yetKuan Wang et al.2019 · 1,024 citations
  2. ToolQA: A Dataset for LLM Question Answering with External ToolsConference on Neural Information Processing Systems (NeurIPS)Yuchen Zhuang, Yue Yu, Kuan Wang, et al.2023 · 182 citations
  3. Reinforcement Learning for Reasoning in Large Language Models with One Training ExampleConference on Neural Information Processing Systems (NeurIPS)Yiping Wang, Qing Yang, Zhiyuan Zeng, et al.2025 · 87 citations
  4. GNN is a Counter? Revisiting GNN for Question Answering ↗arXiv (Cornell University) · no summary yetKuan Wang et al.2021 · 28 citations

 Co-authors here

12 of 15 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 298 times (of the 149,439 papers whose bibliographies we read); 60 citing papers with a page here →.

Updated

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