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Chulin Xie

Chulin Xie has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, Reasoning, Bias, fairness & safety; often with Bowen Jin, Chenhui Zhang, Jiawei Han.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 13 · 42 works · 1,135 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 247 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4038

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

🎓University of Illinois Urbana-ChampaignUrbana · United States2020–2025 · 22 paperscurrent

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

 Concepts, shelves and topics

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

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksSocial Sciences · 1 workSafety Research · 1 workSociology and Political Science · 1 workArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2020s

20241 paperGraph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs20231 paperDecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models

 Papers

The most cited first (OpenAlex and our library).

  1. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models (2023 · 246 citations)
    Practitioners increasingly propose GPT models for high-stakes domains such as healthcare and finance, yet little systematic evidence exists on how reliable these models are.
  2. Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs (2024 · 89 citations)
    LLMs often hallucinate on knowledge-heavy questions. Standard retrieval augmentation treats each document as a separate unit, so it misses knowledge carried by links between texts, such as citations, co-purchases or legal references. Turning whole neighbourhoods into text is also impractical, because subgraphs grow…

All works

3 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. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT ModelsBoxin Wang, Weixin Chen, Hengzhi Pei, et al.Conference on Neural Information Processing Systems (NeurIPS) · 246 citations
  2. Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on GraphsBowen Jin, Chulin Xie, Jiawei Zhang, et al.Findings of the Association for Computational Linguistics · 89 citations
  3. On Memorization of Large Language Models in Logical Reasoning · original ↗ · no summary yet · Proceedings of the 14th International Joint Conference on Natural Language…Chulin Xie et al.2025 · 42 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 247 times (of the 141,840 papers whose bibliographies we read); 17 citing papers with a page here →.

Updated

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