From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 26 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 5Arts and Humanities · 4 works Computer Science · 4 works Artificial Intelligence · 4 works Language and Linguistics · 4 works Computer Vision and Pattern Recognition · 3 works

🧭 Topics 7Evaluation & benchmarks · 2 works Reasoning · 3 works Language models & pretraining · 2 works Multilingual & cross-lingual NLP · 1 work Question answering · 1 work Bias, fairness & safety · 1 work Low-resource languages · 1 work

Works by year

2020s

20254 papers

Papers

The most cited first (OpenAlex and our library).

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025 · 75 citations)
Evaluating LLMs across languages has two gaps. Demanding reasoning benchmarks such as MMLU-Pro exist mainly in English. Multilingual suites like Global-MMLU combine translation sources of uneven quality and pose easier questions. The authors address both with MMLU-ProX, which carries the ten-option, reasoning-heavy…
ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA (2025 · 8 citations)
Multi-hop QA requires chaining evidence from several sources. Standard LLM pipelines, including CoT prompting, retrieval-augmented generation and many multi-agent systems, move only forward. An error in an early hop carries through to the end, and contradictions found later have no structured way to force a revision.…
TLUE: A Tibetan Language Understanding Evaluation Benchmark (2025 · 7 citations)
Tibetan has more than seven million speakers, yet it is largely absent from LLM evaluation. The authors address this with TLUE, which they describe as the first large-scale benchmark of Tibetan language understanding. TLUE has two parts. Ti-MMLU tests general knowledge, and Ti-SafetyBench tests safety behaviour. Both…
TFD: A Comprehensive Structured Tibetan Foundation Dataset for Low-Resource Language Processing and Large-Scale Modeling (2025 · 4 citations)
Tibetan large language models trail far behind those for major languages. Raw text is scarce, but the bigger problem is that no Tibetan resource covers the full pipeline of modern model building, which spans pre-training and instruction tuning, alignment for safety, optimization of preferences, and supervision of…

Co-authors here

12 of 15 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.

Conference on Empirical Methods in Natural Language Processing (EMNLP) ×3: EMNLP 2025 ×3

Cited by

Papers of our library cite their works 80 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

Updated 31 October 2026

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🧑‍🔬 Fan Gao

🏛️ Affiliations

🧭 Fields, topics and concepts

📅 Works by year

2020s

📄 Papers

🤝 Co-authors here

Venues

🔗 Cited by

Fan Gao

Affiliations

Fields, topics and concepts

Works by year

Papers

Co-authors here

Cited by