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Hao Xu

Hao Xu has 4 papers in our library, in 2026, most of them on Language models & pretraining, Vision & language, Efficient models & compression; often with Rui Song, Tianyang Wang, Xi Xiao.

4 papers in our library · 2026

OpenAlex · h-index 1 · 5 works · 1 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 7 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #10097

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

🎓Jilin UniversityChangchun · China2026 · 3 paperscurrent

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 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 3Research subjects and languages the work is about.Efficient models & compression · 1 workLanguage models & pretraining · 2 worksVision & language · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Latent semantic indexing · 1 work

 Works by year

2020s

20264 papers

 Papers

The most cited first (OpenAlex and our library).

  1. HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing Performance (2026 · 5 citations)
    AdaLoRA improves on LoRA by giving different weight matrices different ranks. It does this by expressing each update in SVD form (P, Λ, Q) and pruning unimportant singular values. The cost is slow convergence and heavy computation during training. HyperAdaLoRA addresses this. Instead of updating P, Λ and Q directly…
  2. Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation (2026 · 3 citations)
    The paper argues that LoRA has two weaknesses. First, it spends its small rank budget equally on every low-rank direction, even though many directions carry noise; the authors call this semantic drift. Second, it tunes each layer in isolation, so updates in neighbouring layers can point in unrelated directions; they…
  3. Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning (2026)
    Large multimodal models are increasingly applied to ancient Chinese scripts, but no one has systematically tested whether they can follow how individual characters changed shape over time.
  4. From Fake to Real: Mitigating Out-of-Distribution Bias in In-Context Learning via Feedback Supervision from Large Language Models (2026)
    In-context learning (ICL) relies on labeled demonstrations, usually taken from a source dataset. When test data comes from a very different distribution, these demonstrations can mislead the model. In such out-of-distribution (OOD) settings, similarity-based example selection can even do worse than random choice. The…

All works

6 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing PerformanceFindings of the Association for Computational LinguisticsHao Zhang, Zhenjia Li, Yifan Gao, et al.2026 · 5 citations
  2. Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model AdaptationAnnual Meeting of the Association for Computational Linguistics (ACL)Xi Xiao, Chenrui Ma, Yunbei Zhang, et al.2026 · 3 citations
  3. Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Rui Song, Lida Shi, Ruihua Qi, et al.2026
  4. From Fake to Real: Mitigating Out-of-Distribution Bias in In-Context Learning via Feedback Supervision from Large Language ModelsFindings of the Association for Computational LinguisticsRui Song, Yingji Li, Jian Li, et al.2026
  5. Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetHao Xu et al.2026
  6. ACSE: An Ancient Character Semantic-Aware Embedding for Large Language Models ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetZhihan Zhou et al.2026

 Co-authors here

11 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 7 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

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