---
title: "Huaxiu Yao — author profile and papers — Elamur Research"
description: "Huaxiu Yao, University of North Carolina at Chapel Hill: 3 papers on Elamur Research (2023–2026), h-index 24 — affiliations, topics…"
canonical: "https://research.elamur.ai/free/en/authors/huaxiu-yao/"
---
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# Huaxiu Yao

Huaxiu Yao has 3 papers in our library, in 2023–2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/); often with [Siwei Han](https://md.research.elamur.ai/free/en/authors/siwei-han/), [Baolin Peng](https://md.research.elamur.ai/free/en/authors/baolin-peng/), [Chelsea Finn](https://md.research.elamur.ai/free/en/authors/chelsea-finn/).

3 papers in our library · 2023–2026

[OpenAlex](https://openalex.org/A5051534896) · [ORCID](https://orcid.org/0000-0002-8691-9629) · [**h-index 24**](https://openalex.org/authors/A5051534896) · [172 works](https://openalex.org/works?filter=author.id:A5051534896) · [5,259 citations](https://openalex.org/works?filter=author.id:A5051534896&sort=cited_by_count:desc) OpenAlex, 2026-10-14

[📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/huaxiu-yao/#papers) · [🔗 Cited 1,752 times in our library](https://md.research.elamur.ai/free/en/authors/huaxiu-yao/cited-by/) · [21 more in our library without a page yet](#also) · [⭐ Paper Stars ranking: #6471](https://md.research.elamur.ai/free/en/paper-stars/page/65/#star-6471)

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=Huaxiu+Yao) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Huaxiu+Yao) · [dblp](https://dblp.org/search?q=Huaxiu+Yao) · [ACL Anthology](https://aclanthology.org/search/?q=Huaxiu+Yao)

## Affiliations

[🎓**University of North Carolina at Chapel Hill** Chapel Hill · United States 2022–2026 · 94 papers*current*](https://md.research.elamur.ai/free/en/universities/university-of-north-carolina-at-chapel-hill/) · [🏥**University of North Carolina Health Care** Chapel Hill · United States 2025–2026 · 2 papers*current*](https://ror.org/00qz24g20) · [🎓**Pennsylvania State University** State College · United States 2017–2021 · 24 papers](https://md.research.elamur.ai/free/en/universities/pennsylvania-state-university/) · [🎓**Stanford University** Stanford · United States 2021–2023 · 18 papers](https://md.research.elamur.ai/free/en/universities/stanford-university/) · [🎓**University of California, Santa Cruz** Santa Cruz · United States 2025–2026 · 3 papers](https://md.research.elamur.ai/free/en/universities/university-of-california-santa-cruz/) · [🎓**University of Electronic Science and Technology of China** Chengdu · China 2017–2019 · 3 papers](https://md.research.elamur.ai/free/en/universities/university-of-electronic-science-and-technology-of-china/) · [🎓**Carnegie Mellon University** Pittsburgh · United States 2022–2023 · 2 papers](https://md.research.elamur.ai/free/en/universities/carnegie-mellon-university/) · [🎓**Harvard University** Cambridge · United States 2023 · 2 papers](https://md.research.elamur.ai/free/en/universities/harvard-university/)

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 8 The field of science this work belongs to (OpenAlex fields).[Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 2 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 2 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Social Sciences · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Sociology and Political Science · 1 work](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) · [Computer Vision and Pattern Recognition · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Information Systems · 1 work](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/)

🧭 Topics 2 Research subjects and languages the work is about.[Language models & pretraining · 1 work](https://md.research.elamur.ai/free/en/topics/language-models/) · [Vision & language · 1 work](https://md.research.elamur.ai/free/en/topics/multimodal/)

## Works by year

### 2020s

[**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/huaxiu-yao/year/2026/) · [**2023** 1 paper](https://md.research.elamur.ai/free/en/authors/huaxiu-yao/year/2023/)

## Papers

The most cited first (OpenAlex and our library).

1. **[Fine-tuning Language Models for Factuality](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/)** *(2023 · 127 citations)* LLMs often produce fluent but false statements, especially in long-form text. Checking such output by hand is slow and costly, so supervising factuality with human labels does not scale. The authors fine-tune Llama-2 7B models toward fewer factual errors without any human annotation. For each unlabeled prompt, such…
2. **[SimpleOCR: Rendering Visual Questions to Teach MLLMs to Read](https://md.research.elamur.ai/free/en/papers/peng-2026-simpleocr-rendering-visual-questions-teach/)** *(2026)* The paper asks whether multimodal LLMs actually use their OCR skills during reasoning or mostly rely on the question supplied as text.
3. **[SynthAgent: Adapting Web Agents with Synthetic Supervision](https://md.research.elamur.ai/free/en/papers/wang-2026-synthagent-adapting-web-agents-synthetic/)** *(2026)* Web agents often fail on websites they never saw during training. Such sites rarely come with tasks or demonstrations, and hiring people to write them for each new site is costly. Earlier synthetic-data pipelines fix only one half of the task–trajectory pair. Some produce hallucinated, infeasible goals. Others…

## All works

24 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. **[Holistic Evaluation of Language Models ↗](https://arxiv.org/abs/2211.09110) arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023 · no summary yet**Percy Liang et al.*2022 · 1,001 citations*
2. **[Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction ↗](https://doi.org/10.1609/aaai.v33i01.33015668) Proceedings of the AAAI Conference on Artificial Intelligence · no summary yet**Huaxiu Yao et al.*2019 · 829 citations*
3. **[Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback ↗](https://aclanthology.org/2023.emnlp-main.330/) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yet**Katherine Tian et al.*2023 · 393 citations*
4. **[Few-Shot Knowledge Graph Completion ↗](https://doi.org/10.1609/aaai.v34i03.5698) Proceedings of the AAAI Conference on Artificial Intelligence · no summary yet**Chuxu Zhang et al.*2020 · 234 citations*
5. **[MetaLight: Value-Based Meta-Reinforcement Learning for Traffic Signal Control ↗](https://doi.org/10.1609/aaai.v34i01.5467) Proceedings of the AAAI Conference on Artificial Intelligence · no summary yet**Xinshi Zang et al.*2020 · 169 citations*
6. **[Analyzing and Mitigating Object Hallucination in Large Vision-Language Models ↗](https://arxiv.org/abs/2310.00754) arXiv:2310.00754 · no summary yet**Yiyang Zhou et al.*2023 · 134 citations*
7. [**Fine-tuning Language Models for Factuality arXiv:2311.08401**Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/)
8. **[RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models ↗](https://aclanthology.org/2024.emnlp-main.62/) Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · no summary yet**Peng Xia et al.*2024 · 95 citations*
9. **[Surgical Fine-Tuning Improves Adaptation to Distribution Shifts ↗](https://arxiv.org/abs/2210.11466) arXiv (Cornell University) · no summary yet**Yoonho Lee et al.*2022 · 67 citations*
10. **[Multimodal Representation Learning by Alternating Unimodal Adaptation ↗](https://doi.org/10.1109/cvpr52733.2024.02592) IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · no summary yet**Xiaohui Zhang et al.*2024 · 66 citations*
11. **[Non-Stationary Model for Crime Rate Inference Using Modern Urban Data ↗](https://doi.org/10.1109/tbdata.2017.2786405) IEEE Transactions on Big Data · no summary yet**Hongjian Wang et al.*2017 · 52 citations*
12. **[Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences ↗](https://aclanthology.org/2024.acl-long.25/) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yet**Xiyao Wang et al.*2024 · 48 citations*
13. **[VHELM: A Holistic Evaluation of Vision Language Models ↗](https://doi.org/10.52202/079017-4464) neural information processing systems · no summary yet**Tong Lee et al.*2024 · 34 citations*
14. **[On the Trustworthiness of Generative Foundation Models: Guideline, Assessment, and Perspective ↗](https://arxiv.org/abs/2502.14296) arXiv (Cornell University) · no summary yet**Y. Huang et al.*2025 · 16 citations*
15. **[ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding ↗](https://doi.org/10.52202/085713-5066) neural information processing systems · no summary yet**Yiyang Zhou et al.*2025 · 9 citations*
16. **[Online Structured Meta-learning ↗](https://arxiv.org/abs/2010.11545) arXiv (Cornell University) · no summary yet**Huaxiu Yao et al.*2020 · 9 citations*
17. **[SAFREE: Training-Free and Adaptive Guard for Safe Text-to-Image And Video Generation ↗](https://arxiv.org/abs/2410.12761) arXiv (Cornell University) · no summary yet**Jaehong Yoon et al.*2024 · 8 citations*
18. **[UQ: Assessing Language Models on Unsolved Questions ↗](https://arxiv.org/abs/2508.17580) arXiv (Cornell University) · no summary yet**Nie, Fan et al.*2025 · 6 citations*
19. **[Scaling Agent Learning via Experience Synthesis ↗](https://arxiv.org/abs/2511.03773) arXiv (Cornell University) · no summary yet**Chen, Zhaorun et al.*2025 · 6 citations*
20. **[Position: The Hidden Costs and Measurement Gaps of Reinforcement Learning with Verifiable Rewards ↗](https://arxiv.org/abs/2509.21882) arXiv (Cornell University) · no summary yet**Fang Wu et al.*2025 · 3 citations*
21. **[Anyprefer: An Agentic Framework for Preference Data Synthesis ↗](https://arxiv.org/abs/2504.19276) arXiv (Cornell University) · no summary yet**Yiyang Zhou et al.*2025 · 3 citations*
22. **[Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails ↗](https://arxiv.org/abs/2510.04860) arXiv (Cornell University) · no summary yet**Siwei Han et al.*2025 · 2 citations*
23. **[SimpleOCR: Rendering Visual Questions to Teach MLLMs to Read](https://md.research.elamur.ai/free/en/papers/peng-2026-simpleocr-rendering-visual-questions-teach/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yibo Peng, Peng Xia, Ding Zhong, et al.*2026*
24. **[SynthAgent: Adapting Web Agents with Synthetic Supervision](https://md.research.elamur.ai/free/en/papers/wang-2026-synthagent-adapting-web-agents-synthetic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhaoyang Wang, Yiming Liang, Xuchao Zhang, et al.*2026*

## Co-authors here

12 of 13 co-authors with a page, the most papers together first.

- [Siwei Han](https://md.research.elamur.ai/free/en/authors/siwei-han/) ×2
- [Baolin Peng](https://md.research.elamur.ai/free/en/authors/baolin-peng/)
- [Chelsea Finn](https://md.research.elamur.ai/free/en/authors/chelsea-finn/)
- [Christopher D. Manning](https://md.research.elamur.ai/free/en/authors/christopher-d-manning/)
- [Eric Mitchell](https://md.research.elamur.ai/free/en/authors/eric-mitchell/)
- [Jianfeng Gao](https://md.research.elamur.ai/free/en/authors/jianfeng-gao/)
- [Qianhui Wu](https://md.research.elamur.ai/free/en/authors/qianhui-wu/)
- [Ruiyi Zhang](https://md.research.elamur.ai/free/en/authors/ruiyi-zhang/)
- [Saravan Rajmohan](https://md.research.elamur.ai/free/en/authors/saravan-rajmohan/)
- [Xuchao Zhang](https://md.research.elamur.ai/free/en/authors/xuchao-zhang/)
- [Yibo Peng](https://md.research.elamur.ai/free/en/authors/yibo-peng/)
- [Yiming Liang](https://md.research.elamur.ai/free/en/authors/yiming-liang/)

## Venues

Where their works here were published, the most works first, with the editions of those that have a page.

- Proceedings of the AAAI Conference on Artificial Intelligence ×3
- [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/) ×2: [ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing ×2
- neural information processing systems ×2
- [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/)
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
- IEEE Transactions on Big Data

## Cited by

Papers of our library cite their works 1,752 times (of the 149,439 papers whose bibliographies we read); [357 citing papers with a page here →](https://md.research.elamur.ai/free/en/authors/huaxiu-yao/cited-by/).

Updated 1 November 2026