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Yupeng Cao

Yupeng Cao has 3 papers in our library, in 2025–2026, most of them on Multilingual & cross-lingual NLP, Evaluation & benchmarks; often with Yuechen Jiang, Jimin Huang, Lingfei Qian.

3 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 3 · 12 works · 34 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 20 times in our library · ⭐ Paper Stars ranking: #8427

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

🎓Stevens Institute of TechnologyHoboken · United States2024–2026 · 8 paperscurrent

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

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksDecision Sciences · 1 workManagement Science and Operations Research · 1 workSocial Sciences · 2 worksSociology and Political Science · 2 works

🧭 Topics 2Research subjects and languages the work is about.Evaluation & benchmarks · 1 workMultilingual & cross-lingual NLP · 2 works

 Works by year

2020s

20262 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent (2025 · 29 citations)
    LLM-based agents for finance are usually built for one narrow job, such as trading a single stock or only crypto, and are often tested on proprietary data.
  2. Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026 · 2 citations)
    LLMs are increasingly used to judge financial claims, but existing benchmarks such as FinFact and FinDVer test verification in one fixed setting.
  3. MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model (2026)
    False or manipulated financial content is hard to detect because it often hides in small numerical edits, exaggerated sentiment or inverted cause–effect claims.

 Co-authors here

12 of 16 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 20 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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