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Zixuan Ling

Zixuan Ling has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining; often with Changze Lv, Jen-tse Huang, Michael R. Lyu.

2 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 3 · 14 works · 28 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 30 times in our library · ⭐ Paper Stars ranking: #8219

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

🎓Chinese University of Hong KongHong Kong · China2026 · 1 papercurrent🎓Fudan UniversityShanghai · China2023–2024 · 10 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).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSocial Sciences · 1 workSociology and Political Science · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 2 works

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Aligning Large Language Models with Human Preferences through Representation Engineering (2024 · 37 citations)
    Aligning LLMs with human preferences usually relies on RLHF. RLHF needs extra reward and value models, is expensive and is often unstable to train. Cheaper reward-free alternatives such as DPO and HIR react badly to noisy or mislabeled preference pairs. The authors propose RAHF (Representation Alignment from Human…
  2. Identifying the Achilles' Heel: An Iterative Method for Uncovering Factual Errors in Large Language Models (2026)
    Large language models often state false facts with confidence. Existing factuality benchmarks have four main weaknesses: they need costly manual annotation, their static test sets may have leaked into training data, they cover few topics or question formats, and they cannot steer testing toward a model's weak spots.…

 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 30 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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