Sizhe Wang
Sizhe Wang has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining, Multilingual & cross-lingual NLP, Evaluation & benchmarks; often with Chenming Shang, Chenyang Lu, Dawei Li.
OpenAlex · ORCID · h-index 19 · 85 works · 1,475 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 43 times in our library · ⭐ Paper Stars ranking: #7442
Affiliations
Fields, topics and concepts
📚 Shelves 11The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArts and Humanities · 3 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 1 workComputer Science Applications · 1 workInformation Systems · 1 workHealth Informatics · 1 workHealth Information Management · 1 workHealth Professions · 1 workMedicine · 1 work
🧭 Topics 5Research subjects and languages the work is about.Language models & pretraining · 1 workReasoning · 1 workMultilingual & cross-lingual NLP · 1 workCode & programming languages · 1 workEvaluation & benchmarks · 1 work
Works by year
2020s
Papers
- Can LLMs Learn from Previous Mistakes? Investigating LLMs' Errors to Boost for Reasoning (2024 · 57 citations)
The paper asks whether large language models can improve their reasoning by learning from their own errors, rather than only imitating gold-standard Chain-of-Thought (CoT) rationales. - ShifCon: Enhancing Non-Dominant Language Capabilities with a Shift-based Multilingual Contrastive Framework (2025 · 6 citations)
Multilingual LLMs fine-tuned on translated data (MSFT) still lag behind in non-dominant languages compared with English. Producing more high-quality translated supervision is costly and error-prone. ShifCon addresses this by changing how the model processes these languages internally rather than by adding data. - CodeFlowBench: A Multi-turn, Iterative Benchmark for Complex Code Generation (2026 · 1 citation)
Most code-generation benchmarks test LLMs in a single turn, but real software is built step by step from modular functions that reuse earlier ones. The authors call this incremental, reuse-driven process "codeflow" and present CodeFlowBench as the first benchmark for evaluating it. - CURA: Clinical Uncertainty Risk Alignment for Language Model–Based Risk Prediction (2026)
Fine-tuned clinical language models can predict patient risk from free-text notes, but their confidence scores are often miscalibrated.
Co-authors here
- Chenming Shang
- Chenyang Lu
- Dawei Li
- Dongdong Zhang
- Feiyu Xiong
- Furu Wei
- Hengyuan Zhang
- Jingbo Shang
- Renliang Sun
- Rui Ling
- Wentao Zhang
- Yiyao Yu
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×4: ACL 2026 ×2, ACL 2025, ACL 2024
Cited by
Updated
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