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Xiaolu Zhang

Xiaolu Zhang has 5 papers in our library, in 2025–2026, most of them on Low-resource languages; often with Jun Zhou, Chongxuan Li, Fengqi Zhu.

5 papers in our library · 2025–2026

OpenAlex · h-index 1 · 3 works · 1 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 197 times in our library · ⭐ Paper Stars ranking: #5443

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

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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Diffusion Models (2025 · 178 citations)
    Modern LLMs almost all rely on autoregressive next-token prediction. Many assume that scaling, in-context learning and instruction-following depend on this design. The authors test that assumption with LLaDA, a masked diffusion language model trained from scratch.
  2. LLaDA 1.5: Variance-Reduced Preference Optimization for Large Language Diffusion Models (2026 · 49 citations)
    Masked diffusion models (MDMs) for language cannot compute exact log-likelihoods, so preference alignment methods such as DPO cannot be applied directly.
  3. ReportLogic: Evaluating Logical Quality in Deep Research Reports (2026 · 1 citation)
    LLM-based Deep Research tools produce long analytical reports. Current evaluation mostly checks atomic facts, citations and fluency, so it misses whether the argument holds together and whether conclusions are actually backed. ReportLogic treats logical quality as auditability: can a reader follow, understand and…
  4. Learning to Translate by Translating: Stabilizing the Dual Loop via Semantic-Aware Self-Evolution (2026)
    Supervised LLM translation systems such as Tower and X-ALMA rely on large, expensive human-made parallel corpora.
  5. Reinforcement Learning with Semantic Rewards Enables Low-Resource Language Expansion without Alignment Tax (2026)
    When LLMs are adapted to a poorly supported language, they often lose ground on skills they already had. This is known as the alignment tax. The authors blame supervised fine-tuning (SFT): its token-level likelihood objective forces the model to copy small, skewed datasets, which overwrites pretrained knowledge. They…

 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 197 times (of the 149,439 papers whose bibliographies we read); 33 citing papers with a page here →.

Updated

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