--- title: "Jinman Zhao · University of Toronto — author profile and papers" description: "Jinman Zhao, University of Toronto: 6 papers on Elamur Research (2024–2026), h-index 9 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/jinman-zhao/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jinman-zhao/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Jinman Zhao # Jinman Zhao Jinman Zhao has 6 papers in our library, in 2024–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Reasoning](https://md.research.elamur.ai/free/en/topics/reasoning/), [Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/); often with [Yining Wang](https://md.research.elamur.ai/free/en/authors/yining-wang/), [Jiaru Li](https://md.research.elamur.ai/free/en/authors/jiaru-li/), [Linbo Cao](https://md.research.elamur.ai/free/en/authors/linbo-cao/). 6 papers in our library · 2024–2026 [OpenAlex](https://openalex.org/A5036110657) · [ORCID](https://orcid.org/0009-0001-1511-8875) · [**h-index 9**](https://openalex.org/authors/A5036110657) · [35 works](https://openalex.org/works?filter=author.id:A5036110657) · [406 citations](https://openalex.org/works?filter=author.id:A5036110657&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 6 works in our library](https://md.research.elamur.ai/free/en/authors/jinman-zhao/#papers) · [🔗 Cited 48 times in our library](https://md.research.elamur.ai/free/en/authors/jinman-zhao/cited-by/) · [⭐ Paper Stars ranking: #8921](https://md.research.elamur.ai/free/en/paper-stars/page/90/#star-8921) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Jinman+Zhao) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Jinman+Zhao) · [dblp](https://dblp.org/search?q=Jinman+Zhao) · [ACL Anthology](https://aclanthology.org/search/?q=Jinman+Zhao) ## Affiliations [🎓**University of Toronto** Toronto · Canada 2021–2026 · 13 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-toronto/) · [🎓**University of Wisconsin–Madison** Madison · United States 2015–2021 · 7 papers](https://md.research.elamur.ai/free/en/universities/university-of-wisconsin-madison/) · [🎓**Hebei University of Science and Technology** Shijiazhuang · China 2023–2024 · 4 papers](https://ror.org/05h3pkk68) · [🏢**Amazon** Seattle · United States 2023–2024 · 3 papers](https://md.research.elamur.ai/free/en/companies/amazon/) · [🎓**Hebei Agricultural University** Baoding · China 2022 · 2 papers](https://ror.org/009fw8j44) From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed. ## Fields, topics and concepts Of its works in our library, with the number of works on each. 📚 Shelves 9 The field of science this work belongs to (OpenAlex fields).[Computer Science · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 5 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Artificial Intelligence · 6 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Language and Linguistics · 5 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Safety Research · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · [Social Sciences · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Software · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/software/) 🧭 Topics 3 Research subjects and languages the work is about.[Language models & pretraining · 2 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Bias, fairness & safety · 1 work](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/) · [Reasoning · 1 work](https://md.research.elamur.ai/free/en/topics/reasoning/) 💡 Concepts 1 Key ideas and methods explained on our concept pages.[Latent semantic indexing · 1 work](https://md.research.elamur.ai/free/en/concepts/latent-semantic-indexing/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/jinman-zhao/year/2026/) · [**2025** 3 papers](https://md.research.elamur.ai/free/en/authors/jinman-zhao/year/2025/) · [**2024** 1 paper](https://md.research.elamur.ai/free/en/authors/jinman-zhao/year/2024/) ## Papers The most cited first (OpenAlex and our library). 1. **[LongRecipe: Recipe for Efficient Long Context Generalization in Large Language Models](https://md.research.elamur.ai/free/en/papers/hu-2025-longrecipe-recipe-efficient-long-context/)** *(2025 · 16 citations)* Large language models handle inputs poorly once these exceed the context length seen during pretraining. Continuing training on full-length sequences fixes this but is expensive, since attention cost grows quadratically with length. LongRecipe is a training framework that extends the context window without training… 2. **[UORA: Uniform Orthogonal Reinitialization Adaptation in Parameter Efficient Fine-Tuning of Large Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-uora-uniform-orthogonal-reinitialization-adaptation/)** *(2025 · 14 citations)* Fully fine-tuning large models is expensive in compute and in storage for per-task checkpoints. LoRA lowers this cost by training low-rank matrices. VeRA goes further: it freezes random projection matrices and trains only two scaling vectors, but it needs a high rank to stay competitive, which raises computation. The… 3. **[Role-Play Paradox in Large Language Models: Reasoning Performance Gains and Ethical Dilemmas](https://md.research.elamur.ai/free/en/papers/zhao-2024-role-play-paradox-large-language/)** *(2024 · 9 citations)* Role-play prompting, where an LLM is told to act as a teacher, scriptwriter, or even an object like a coin, is known to improve reasoning. 4. **[Tiny Budgets, Big Gains: Parameter Placement Strategy in Parameter Super-Efficient Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhao-2025-tiny-budgets-big-gains-parameter/)** *(2025 · 8 citations)* Parameter-efficient fine-tuning methods such as LoRA lower the cost of adapting large models. Yet models now reach hundreds of billions of parameters, so even rank-1 LoRA can be too heavy for mobile hardware or labs with little compute. The authors ask how a very small, fixed budget of trainable parameters should be… 5. **[CodeHacker: Automated Test Case Generation for Detecting Vulnerabilities in Competitive Programming Solutions](https://md.research.elamur.ai/free/en/papers/shi-2026-codehacker-automated-test-case-generation/)** *(2026 · 7 citations)* Code benchmarks for LLMs often have test suites that miss subtle corner cases, so flawed programs are wrongly marked correct. 6. **[On the Representation Geometry of LoRA Model Merging](https://md.research.elamur.ai/free/en/papers/lu-2026-representation-geometry-lora-model-merging/)** *(2026)* Combining several task-specific LoRA adapters into one model without further training is hard because their low-rank updates tend to occupy misaligned subspaces. ## Co-authors here 12 of 20 co-authors with a page, the most papers together first. - [Yining Wang](https://md.research.elamur.ai/free/en/authors/yining-wang/) ×3 - [Jiaru Li](https://md.research.elamur.ai/free/en/authors/jiaru-li/) ×2 - [Linbo Cao](https://md.research.elamur.ai/free/en/authors/linbo-cao/) ×2 - [Xueyan Zhang](https://md.research.elamur.ai/free/en/authors/xueyan-zhang/) ×2 - [Yulan Hu](https://md.research.elamur.ai/free/en/authors/yulan-hu/) ×2 - [Anh Tuan Luu](https://md.research.elamur.ai/free/en/authors/anh-tuan-luu/) - [Bryan Hooi](https://md.research.elamur.ai/free/en/authors/bryan-hooi/) - [Chenyang Lu](https://md.research.elamur.ai/free/en/authors/chenyang-lu/) - [Erxue Min](https://md.research.elamur.ai/free/en/authors/erxue-min/) - [Gerald Penn](https://md.research.elamur.ai/free/en/authors/gerald-penn/) - [Jing Huang](https://md.research.elamur.ai/free/en/authors/jing-huang/) - [Qing Gu](https://md.research.elamur.ai/free/en/authors/qing-gu/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×3: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/), [ACL 2025](https://md.research.elamur.ai/free/en/venues/acl/2025/) ×2 - [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/): [EMNLP 2025](https://md.research.elamur.ai/free/en/venues/emnlp/2025/) - [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/): [Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/) ## Cited by Papers of our library cite their works 48 times (of the 149,439 papers whose bibliographies we read); [16 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/jinman-zhao/cited-by/). Updated 1 November 2026