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.
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
Affiliations
Fields, topics and concepts
📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 workHistory and Philosophy of Science · 1 work
🧭 Topics 1Research subjects and languages the work is about.Low-resource languages · 1 work
Works by year
2020s
Papers
- 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. - 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. - 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… - 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. - 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
- Jun Zhou ×4
- Chongxuan Li ×2
- Fengqi Zhu ×2
- Ji-Rong Wen ×2
- Shen Nie ×2
- Yankai Lin ×2
- Chenliang Li
- Jun Hu
- Suzan Verberne
- Wentao Zhang
- Zhaochun Ren
- Zhou Liu
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026 ×2
- Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
- Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2025
Cited by
Updated
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