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Peerat Limkonchotiwat

Peerat Limkonchotiwat has 4 papers in our library, in 2025–2026, most of them on Multilingual & cross-lingual NLP, Bias, fairness & safety, Evaluation & benchmarks; often with Jian Gang Ngui, Alham Fikri Aji, Panuthep Tasawong.

4 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 8 · 103 works · 245 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 270 times in our library · ⭐ Paper Stars ranking: #5587

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

🏛️AI SingaporeSingapore · Singapore2024–2026 · 28 papersEmployment · Research Fellow, AI productcurrent🎓Vidyasirimedhi Institute of Science and TechnologyRayong · Thailand2020–2026 · 25 paperscurrent🎓National University of SingaporeSingapore · Singapore2024–2026 · 9 paperscurrent🏢GoogleMountain View · United States2026 · 4 paperscurrent🎓Chulalongkorn UniversityBangkok · Thailand2021–2026 · 3 paperscurrent🎓Nanyang Technological UniversitySingapore · Singapore2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksComputer Science · 4 worksArts and Humanities · 2 worksLanguage and Linguistics · 2 worksSafety Research · 3 worksSocial Sciences · 3 works

🧭 Topics 3Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 2 worksBias, fairness & safety · 2 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20262 papers20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation (2025 · 182 citations)
    Multilingual LLMs are often evaluated on machine-translated copies of English MMLU. This adds translation artifacts and keeps a US- and Europe-centred knowledge bias that may not matter to speakers of other languages. The authors measure this bias and build an alternative. Around 200 annotators, both paid…
  2. SEA-LION: Southeast Asian Languages in One Network (2025 · 29 citations)
    Most open LLMs are designed around English and perform poorly on Southeast Asian languages. Lower-resource ones such as Burmese, Lao, Khmer and Filipino are hit hardest, and even regional models like SeaLLMs and Sailor fall short on Thai and Tamil. The AI Singapore team responds with SEA-LION, two instruction-tuned…
  3. SEA-Guard: Culturally Grounded Multilingual Safeguard for Southeast Asia (2026)
    Most safety classifiers that screen LLM inputs and outputs are built for English. Multilingual ones usually depend on machine-translated English data, which serves Southeast Asian languages poorly and misses local cultural, religious and regulatory sensitivities. The authors address this gap with an agentic synthesis…
  4. SEA-SafeguardBench: Culturally Grounded Safety Benchmark for Southeast Asian Languages (2026)
    Safeguard models that screen LLM inputs and outputs for harm are mostly tested in English. Multilingual safety benchmarks usually rely on machine translation, which can distort low-resource languages and miss local cultural risks. Southeast Asia has especially little coverage. SEA-SafeguardBench addresses this gap…

 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.

 Cited by

Papers of our library cite their works 270 times (of the 149,439 papers whose bibliographies we read); 75 citing papers with a page here →.

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