Elamur Research⌕
☰
home / authors / Chejian Xu

Chejian Xu

Chejian Xu has 2 papers in our library, in 2023–2026, most of them on Language models & pretraining, Bias, fairness & safety; often with Boxin Wang, Bo Li, Bryan Catanzaro.

2 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 8 · 21 works · 650 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 183 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5847

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 States2022–2026 · 12 paperscurrent🎓Zhejiang UniversityHangzhou · China2021–2022 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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 8The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksSocial Sciences · 1 workArtificial Intelligence · 2 worksSafety Research · 1 workSociology and Political Science · 1 workArts and Humanities · 1 workComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2020s

20261 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models (2023 · 259 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. From 128K to 4M: Efficient Training of Ultra-Long Context Large Language Models (2026 · 5 citations)
    Open LLMs struggle to reason over very long inputs. Existing context-extension efforts often lack transparent data recipes, require expensive training, or lose quality on ordinary short tasks. The authors present a two-stage recipe that takes Llama-3.1-8B-Instruct from its native 128K window to 1M, 2M, and 4M tokens.

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. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT ModelsConference on Neural Information Processing Systems (NeurIPS)Boxin Wang, Weixin Chen, Hengzhi Pei, et al.2023 · 259 citations
  2. From 128K to 4M: Efficient Training of Ultra-Long Context Large Language ModelsFindings of the Association for Computational LinguisticsChejian Xu, Wei Ping, Peng Xu, et al.2026 · 5 citations
  3. MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models ↗arXiv (Cornell University) · no summary yetChejian Xu et al.2025 · 5 citations
  4. DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents ↗arXiv (Cornell University) · no summary yetZhaorun Chen et al.2026

 Co-authors here

12 of 13 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 183 times (of the 149,439 papers whose bibliographies we read); 45 citing papers with a page here →.

Updated

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