Research Project
Single-Pass Document Scanning for Question Answering
Training state space models for long-context question answering that scan documents once and answer efficiently.
Research
Projects and Publications
My research focuses on efficient methods for natural language processing, including retrieval-augmented language models and state space models for long-context question answering.
Projects
2 project pagesResearch Project
How Novices Use Program Visualizations to Understand Code that Manipulates Data Tables
A computing education study of how novice programmers use visualizations to reason about data-table code.
Publications
Single-Pass Document Scanning for Question Answering
arXiv Preprint, 2025
TL;DR: We trained State Space Models (Mamba-2) for long-context Q&A, achieving performance comparable to GPT-4o on extremely long documents while being more computationally efficient.
@inproceedings{cao2025singlepassdocumentscanning, title={Single-Pass Document Scanning for Question Answering}, author={Cao, Weili and Wang, Jianyou and Zheng, Youze and Bao, Longtian and Zheng, Qirui and Berg-Kirkpatrick, Taylor and Paturi, Ramamohan and Bergen, Leon}, booktitle={Proceedings of the 2025 Conference on Language Modeling (COLM)}, year={2025}, }How Novices Use Program Visualizations to Understand Code that Manipulates Data Tables
Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE TS 25), ACM, 2025
TL;DR: A case study on how novices use program visualizations to understand code manipulating data tables, revealing the importance of visualizations in learning programming.
@inproceedings{wu2025novices, title={How Novices Use Program Visualizations to Understand Code that Manipulates Data Tables}, author={Wu, Ylesia and Zheng, Qirui and Lau, Sam}, booktitle={Proceedings of the 56th ACM Technical Symposium on Computer Science Education V. 1}, pages={1267--1273}, year={2025} }