From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 35 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 7Artificial Intelligence · 2 works Arts and Humanities · 2 works Computer Science · 2 works Language and Linguistics · 2 works Safety Research · 1 work Social Sciences · 1 work Computer Vision and Pattern Recognition · 1 work

🧭 Topics 3Language models & pretraining · 2 works Bias, fairness & safety · 1 work Evaluation & benchmarks · 1 work

Works by year

2020s

20261 paper 20231 paper

Papers

The most cited first (OpenAlex and our library).

CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility (2023 · 64 citations)
Existing Chinese LLM benchmarks such as C-EVAL, M3KE and GAOKAO-Bench mostly test knowledge and reasoning.
MDC-Bench: A Multidisciplinary Causal Benchmark Based on Causal Structures for Evaluating Large Language Models (2026)
Most existing causal benchmarks for LLMs use everyday commonsense scenarios with short, single-hop cause–effect links.

Co-authors here

12 of 14 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.

Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026

Cited by

Papers of our library cite their works 52 times (of the 149,439 papers whose bibliographies we read); 17 citing papers with a page here →.

Updated 1 November 2026

This page in Markdown → https://md.research.elamur.ai/free/en/authors/chao-peng/

🧑‍🔬 Chao Peng

🏛️ Affiliations

🧭 Fields, topics and concepts

📅 Works by year

2020s

📄 Papers

🤝 Co-authors here

Venues

🔗 Cited by

Chao Peng

Affiliations

Fields, topics and concepts

Works by year

Papers

Co-authors here

Cited by