My (Chiffon) Nguyen

My (Chiffon) Nguyen she/her

Nguyễn Trà My / 阮沐茶 / 윈자미

AI Research for Broader World & Life-long Learning

I research current and future AI that are safe and empowering for more people. Towards this end, I’m currently interested in the following problems:

My technical work focuses on data, simulation, and evaluation, to make grounded, predictive, specific claims about AI capability and safety, then improve them.

I’m doing research for Lida Safety. I also contribute to community research, including Cohere Labs Community (leadership & analysis), and BenchFlow (eval infrastructure). Recently I worked on SEATauBench (EMNLP Findings ’26), and CoT monitoring behavior.

I am seeking research Master’s in CS, AI, or NLP for Fall 2027. Open to research collaborations.

Latest updates

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My first co-first-authored paper, SEATauBench, has been accepted to EMNLP Findings 2026.

Selected publications

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MultiCulturalRiddle: A Multicultural Benchmark of Riddles

Tianyi Hu*, Henry Gagnier*, Vinod Anbalagan*, My Chiffon Nguyen*, Pouya Sadeghi*, Farah Abdou*, , Károly Boczka, Julia Kreutzer
Under review • 2026
Abstract
Capturing how well LLMs can understand and model the diversity of cultures around the globe has increasingly gained importance as LLMs' language coverage has rapidly advanced. However, existing benchmarks are still knowledge- and English-centric with limited coverage and complexity. We propose MultiCulturalRiddle, a benchmark of culturally-grounded riddles, spanning 61 cultures and 51 languages, created in a participatory community effort. These riddles are both hyper-specific to each culture, require factual knowledge, language skills, social knowledge, and strong abductive reasoning skills to solve. We benchmark 24 LLMs with both automatic and human evaluation and release all artifacts publicly.
Evaluation/Benchmarking Multilinguality Cultural Adaptation

GPS-Bench: A Governance Policy Simulator for Automating Policy Analysis

Linh Le, Melanie Bui, My Chiffon Nguyen, Zachary Schlosser, David Williams-King
Under review • 2026
Abstract
Policy analysis requires more than predicting whether a proposal will pass: it requires identifying who will be affected, how those actors respond, and what follows. LLM-based policy simulations model these processes at scale, but their validity is hard to establish when plausible behaviour is never compared with observed outcomes. We introduce GPS-Bench, an evidence-grounded benchmark for governance policy simulation that links policies to relevant actors, actor actions and downstream impacts using legislative records, lobbying disclosures, regulatory documents, corporate filings, economic data and other public evidence. Actors are reconstructed from the dated record rather than prompted as archetypes, so a persona is an evidence object with provenance; a human-annotated pool forms the Gold evaluation set, while cases labelled by a separate LLM from retrieved evidence are treated as Silver supervision and never as test labels. Because every inference mode reads the same grounded state and emits the same schema, GPS-Bench turns 'does multi-agent simulation help?' into a controlled comparison: we contrast joint reasoning, independent and communicating actor agents, graph-based methods and weight-level fine-tuning over one policy state. Fine-tuning on the grounded record gives the strongest actor-level impact prediction, and decomposition does not beat it; what decomposition adds is mechanism. Agents hold private, non-identical evidence, each seeing its own exposure clause, and address named partners with concrete joint proposals—what they offer, what they need in return, and why acting together beats acting alone—so the coalitions that form can be checked against the commitments the record holds. GPS-Bench therefore gives a common empirical setting for studying when evidence, actor modelling and multi-agent interaction improve the prediction and interpretation of policy outcomes.
Evaluation/Benchmarking AI Governance Agent

SEATauBench: Adapting Tool-Agent-User Evaluation Into Low-Resource Southeast Asian Languages

My Chiffon Nguyen*, Aulia Adila*, Saksorn Ruangtanusak*, Kittiphat Leesombatwathana*, Vissuta Gunawan Lim*, Patomporn Payoungkhamdee, Samuel Cahyawijaya
EMNLP Findings • 2026
Abstract
We introduce SEATauBench, the first agentic-focused evaluation framework for sovereign AI development in Southeast Asia, a region of strategic importance with over 700 million people. Despite growing regional evaluation efforts, existing multilingual agents show limited capability when operating in SEA languages, particularly in mixed-language scenarios. Through evaluation across multiple adaptation approaches, we find that while English agentic capabilities transfer to target language responses, performance degrades significantly when context is provided in SEA languages. We propose a translation-based mitigation strategy that preserves entity consistency while enabling agents to leverage English comprehension. SEATauBench establishes a rigorous benchmark for sovereign AI agent assessment, providing diagnostic tools to address capability gaps and support agentic AI development in diverse linguistic communities in the region.
PDF
Evaluation/Benchmarking Agent Multilinguality

Career highlights

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Miscellaneous

  • My Vietnamese name is ‘Trà My’, which is Camellia japonica (tea flower).

  • I graduated in May 2025 from Minerva University studying Machine Learning & Statistics, with peers from 50+ countries and living in 6 cities as part of its (old) global immersion model: Seoul (South Korea), Taipei, Hyderabad (India), Buenos Aires (Argentina), Berlin (Germany). This experience heavily shapes my worldviews and motivates my research focus in diversity and collaboration.

  • I like reading, cooking, matcha & oolong tea, cycling, teaching, travel (cultural activities & historical museums), event organizing, philosophy, and politics.

  • Other than Vietnamese and English, I speak a bit of Mandarin Chinese (HSK4/B1) and French (A2).

  • My friends asked me to collect tech recommendation in a page: here.

  • Donate to charities if you can afford it! Some options are World Food Programme, Doctors Without Borders, and Giving What We Can.