Chenghao Xiao
Chenghao Xiao has 3 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning; often with Noura Al Moubayed, Chenghua Lin, Deli Zhao.
OpenAlex · ORCID · h-index 5 · 53 works · 70 citations OpenAlex, 2026-10-14
📄 3 works in our library · 🔗 Cited 16 times in our library · ⭐ Paper Stars ranking: #8915
Affiliations
Fields, topics and concepts
📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksMedicine · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksHealth Informatics · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workBiochemistry, Genetics and Molecular Biology · 1 workMolecular Biology · 1 work
🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1 workLanguage models & pretraining · 1 work
Works by year
2020s
Papers
- ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning (2025 · 11 citations)
Reasoning-oriented LLMs do well in math and code, but medical QA depends on broad, accurate domain knowledge and lacks large, carefully checked reasoning data. - Towards more Human-like Language Models based on Contextualizer Pretraining Strategy (2023 · 7 citations)
The paper asks how to pretrain a capable masked language model when data is as scarce as what a child hears, within the BabyLM Challenge. - X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation (2026 · 4 citations)
Chest X-ray reports are written in dense clinical jargon and follow rigid templates. This creates two problems. Patients struggle to understand reports they now often see before talking to a doctor. Models can also score well on n-gram metrics like BLEU simply by copying recurring report phrasing. The authors propose…
Co-authors here
Venues
- Conference on Computational Natural Language Learning (CoNLL): CoNLL 2023
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2025
- Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
Cited by
Updated
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