collaborators

6 papers

cs.LG2026

Capacity-Dependent Effects of Data Selection for Reasoning

Cuong Dang, Hoang Anh Just, Ruoxi Jia

In reasoning supervised fine-tuning, candidate responses for the same instruction can differ substantially in how well they match the student's current distribution. Recent likelih…

cs.CL2026

Retrieval-Augmented Long-Context Translation for Cultural Image Captioning: Gators submission for AmericasNLP 2026 shared task

Aashish Dhawan, Christopher Driggers-Ellis, Dzmitry Kasinets +2

We present the University of Florida Gators submission to the AmericasNLP 2026 shared task on cultural image captioning for Indigenous languages. Our two-stage pipeline generates a…

cs.LG2026

Semantic Similarity is a Spurious Measure of Comic Understanding: Lessons Learned from Hallucinations in a Benchmarking Experiment

Christopher Driggers-Ellis, Nachiketh Tibrewal, Rohit Bogulla +4

A system that enables blind or visually impaired users to access comics/manga would introduce a new medium of storytelling to this community. However, no such system currently exis…

cs.LG2026

RISE: Interactive Visual Diagnosis of Fairness in Machine Learning Models

Ray Chen, Christan Grant

Evaluating fairness under domain shift is challenging because scalar metrics often obscure exactly where and how disparities arise. We introduce \textit{RISE} (Residual Inspection…

cs.CL2026

Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing

Aashish Dhawan, Christopher Driggers-Ellis, Christan Grant +1

Low-resource indigenous languages often lack the parallel corpora required for effective neural machine translation (NMT). Synthetic data generation offers a practical strategy for…

cs.CL2025

MultiScript30k: Leveraging Multilingual Embeddings to Extend Cross Script Parallel Data

Christopher Driggers-Ellis, Detravious Brinkley, Ray Chen +3

Multi30k is frequently cited in the multimodal machine translation (MMT) literature, offering parallel text data for training and fine-tuning deep learning models. However, it is l…