Nhi N. Truong

DePauw University

Greencastle, Indiana

Hi! I’m Nhi, a Computer Science and Economics student at DePauw University.

I currently work with Professor Sanmi Koyejo at the Stanford Trustworthy AI Research Lab, where I help build evaluation frameworks for large language models. Previously, I worked on time series forecasting at the VNU-HCMUT AI Lab and contributed to The R Project through Google Summer of Code.

I’m interested in machine learning, natural language processing, and time series forecasting, especially their applications in healthcare and education.

News

Jun 01, 2024	Selected as a Google Summer of Code 2024 contributor for The R Project for Statistical Computing, working on the animint2 package.
Mar 13, 2024	Our paper “Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models” was accepted at NAACL 2024! Featured in The New York Times and Stanford HAI.
Mar 08, 2024	Our paper “Hybrid Transformer and Holt-Winter’s Method for Time Series Forecasting” was accepted at the Time Series for Health workshop at ICLR 2024!

Selected Publications

NAACL

Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models

Nhi N. Truong, Hien Vo, Nhat Tran, and 3 more authors

In Findings of the Association for Computational Linguistics: NAACL 2024. Featured in The New York Times and Stanford HAI. , Mar 2024

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Recent advancements in large language models (LLMs) have underscored their importance in the evolution of artificial intelligence. However, despite extensive pretrained on multilingual datasets, available open-sourced LLMs exhibit limited effectiveness in processing Vietnamese. The challenge is exacerbated by the absence of systematic benchmark datasets and metrics tailored for Vietnamese LLM evaluation. To mitigate these issues, we have finetuned LLMs specifically for Vietnamese and developed a comprehensive evaluation framework encompassing 10 common tasks and 31 metrics. Our evaluation results reveal that the fine-tuned LLMs exhibit enhanced comprehension and generative capabilities in Vietnamese. Moreover, our analysis indicates that models with more parameters can introduce more biases and uncalibrated outputs and the key factor influencing LLM performance is the quality of the training or fine-tuning datasets. These insights underscore the significance of meticulous fine-tuning with high-quality datasets in enhancing LLM performance.
ICLR WS

Hybrid Transformer and Holt-Winter’s Method for Time Series Forecasting

Nhi N. Truong, Duc Q. Nguyen, Jeffrey Gropp, and 1 more author

In Time Series for Health Workshop, International Conference on Learning Representations (ICLR), Mar 2024

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Time series forecasting is an important research topic in machine learning due to its prevalence in social and scientific applications. Multi-model forecasting paradigm, including model hybridization and model combination, is shown to be more effective than single-model forecasting in the M4 competition. In this study, we hybridize exponential smoothing with transformer architecture to capture both levels and seasonal patterns while exploiting the complex non-linear trend in time series data. We show that our model can capture complex trends and seasonal patterns with moderately improvement in comparison to the state-of-the-arts result from the M4 competition.