62 citations · 114 across the 16 of their papers we have counts for
6 papers · 1 filter
A Dataset for Modeling Iterative Problem-Solving
Fagun Patel, Sang T. Truong, Duc Q. Nguyen +4
Solving problems through repeated attempts is a sequential modeling task: at each step, the solver receives feedback and decides how to revise their solutions. Predicting whether p…
Why Do Safety Guardrails Degrade Across Languages?
Max Zhang, Ameen Patel, Sang T. Truong +1
Large language models exhibit safety degradation in non-English languages. Standard evaluation relies on Jailbreak Success Rate (JSR), which confounds several safety-driving factor…
In-Situ Behavioral Evaluation for LLM Fairness, Not Standardized-Test Scores
Zeyu Tang, Sang T. Truong, Deonna Owens +4
LLM fairness should be evaluated through in-situ behavioral pattern rather than standardized-test Q&A benchmarks. We show that the standardized-test paradigm can be structurally un…
Crossing Linguistic Horizons: Finetuning and Comprehensive Evaluation of Vietnamese Large Language Models
Sang T. Truong, Duc Q. Nguyen, Toan Nguyen +4
Recent advancements in large language models (LLMs) have underscored their importance in the evolution of artificial intelligence. However, despite extensive pretraining on multili…
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models
Gantavya Bhatt, Yifang Chen, Arnav M. Das +9
Supervised finetuning (SFT) on instruction datasets has played a crucial role in achieving the remarkable zero-shot generalization capabilities observed in modern large language mo…
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Boxin Wang, Weixin Chen, Hengzhi Pei +16
Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in their capabilities, capturing the interest of practitioners and the public alike. Yet, while the…