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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.

4 papers in our library · 2024–2026

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

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 9 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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. 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.
  2. 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.
  3. 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.
  4. 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

12 of 17 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 43 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

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