1 citations · 1 across the 4 of their papers we have counts for
7 papers
SWE-MISBEHAVIOR: Benchmarking and Mitigating Boundary Violations in Software Coding Agents
Do Xuan Trong, Duc Anh Nguyen, Do Xuan Long +12
We present SWE-MISBEHAVIOR, the first taxonomy and evaluation framework for diagnosing boundary-violating misbehaviors in coding agents. It contains 7.4K human-in-the-loop synthesi…
Multi-Marginal Inverse Optimal Transport for Contrastive Learning Via Explicit Anchor-Positive-Negative Coupling
Ngoc-Hai Nguyen, Thuan Nguyen, Prakash Ishwar +1
Inverse Optimal Transport (OT) based methods for representation learning learn representations such that the global OT coupling between a pair of data marginals in the representati…
Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions
Quyen Tran, Hai Nguyen, Quan Dao +4
Analytic Continual Learning (ACL) offers a computationally efficient alternative to gradient-based approaches. Recent ACL methods are based on Recursive Least Squares (RLS) and hav…
What Makes a Good Natural Language Prompt?
Do Xuan Long, Duy Dinh, Ngoc-Hai Nguyen +4
As large language models (LLMs) have progressed towards more human-like and human--AI communications have become prevalent, prompting has emerged as a decisive component. However,…
On Barycenter Computation: Semi-Unbalanced Optimal Transport-based Method on Gaussians
Ngoc-Hai Nguyen, Dung Le, Hoang-Phi Nguyen +2
We explore a robust version of the barycenter problem among centered Gaussian probability measures, termed Semi-Unbalanced Optimal Transport (SUOT)-based Barycenter, wherein th…
LLMs Are Biased Towards Output Formats! Systematically Evaluating and Mitigating Output Format Bias of LLMs
Do Xuan Long, Hai Nguyen Ngoc, Tiviatis Sim +5
We present the first systematic evaluation examining format bias in performance of large language models (LLMs). Our approach distinguishes between two categories of an evaluation…
An Optimal Transport-driven Approach for Cultivating Latent Space in Online Incremental Learning
Quyen Tran, Hai Nguyen, Hoang Phan +6
In online incremental learning, data continuously arrives with substantial distributional shifts, creating a significant challenge because previous samples have limited replay valu…