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Zidi Xiong

Zidi Xiong has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Bias, fairness & safety; often with Boxin Wang, Chejian Xu, Chenhui Zhang.

3 papers in our library · 2023–2026

OpenAlex · h-index 3 · 21 works · 114 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 205 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5158

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

🎓Harvard UniversityCambridge · United States2025–2026 · 10 paperscurrent🎓University of Illinois Urbana-ChampaignUrbana · United States2024 · 2 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksSocial Sciences · 2 worksArtificial Intelligence · 3 worksSafety Research · 2 worksSociology and Political Science · 1 workArts and Humanities · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20262 papers20231 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. How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following Behavior (2026 · 13 citations)
    LLM agents often keep an episodic memory of earlier task queries and their execution trajectories, then retrieve similar records as in-context demonstrations.
  3. User-Assistant Bias in LLMs (2026)
    Chat LLMs split their context into role-tagged segments such as system, user, assistant and tool. This paper asks whether the tags themselves make a model weigh information unequally. The authors define user–assistant bias as follows: when the user and the assistant earlier gave contradictory attributes for the same…

All works

5 works of theirs in our library, the most cited first; 3 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. RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content ↗arXiv (Cornell University) · no summary yetZhuowen Yuan et al.2024 · 19 citations
  3. How Memory Management Impacts LLM Agents: An Empirical Study of Experience-Following BehaviorAnnual Meeting of the Association for Computational Linguistics (ACL)Zidi Xiong, Yuping Lin, Wenya Xie, et al.2026 · 13 citations
  4. MMDT: Decoding the Trustworthiness and Safety of Multimodal Foundation Models ↗arXiv (Cornell University) · no summary yetChejian Xu et al.2025 · 5 citations
  5. User-Assistant Bias in LLMsFindings of the Association for Computational LinguisticsXu Pan, Jingxuan Fan, Zidi Xiong, et al.2026

 Co-authors here

12 of 14 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 205 times (of the 149,439 papers whose bibliographies we read); 45 citing papers with a page here →.

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