National Taiwan University Papers

Browse 3 peer-reviewed papers from National Taiwan University spanning Crowdsourcing, Data Quality (2024–2025). Research powered by Prolific's high-quality participant data.

This page lists 3 peer-reviewed papers from researchers at National Taiwan University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (3 of 3)

Data quality in crowdsourcing and spamming behavior detection

Authors: Y Ba, MV Mancenido, EK Chiou, R Pan

Year: 2025

Published in: Behavior Research Methods, 2025 - Springer

Institution: University of Delaware, National Taiwan University, University of British Columbia, Monash University

Research Area: Crowdsourcing, Data Quality, Spamming Behavior Detection, LLM Applications in Behavioral Research

Discipline: Computer Science, Artificial Intelligence, Large Language Models

The paper introduces a systematic method to evaluate crowdsourced data quality and detect spam behaviors through variance decomposition, proposing a spammer index and credibility metrics to improve consistency and reliability in labeling tasks.

Methods: Variance decomposition, Markov chain models, and generalized random effects models were used to assess annotator consistency and credibility; metrics were applied to both simulated and real-world data from two crowdsourcing platforms.

Key Findings: Quality of crowdsourced data, spammer behaviors, annotators’ consistency, and credibility.

Citations: 2
MotionMatcher: Motion Customization of Text-to-Video Diffusion Models via Motion Feature Matching

Authors: Y Wu, C Huang, F Yang, F Wang

Year: 2025

Published in: ArXiv

Institution: Nvidia, National Taiwan University

Research Area: Motion Customization of Text-to-Video Diffusion Models

Discipline: Computer Vision, Pattern Recognition

MotionMatcher is a novel framework for motion customization in text-to-video (T2V) diffusion models, using high-level spatio-temporal motion features rather than pixel-level objectives, achieving state-of-the-art performance.

Methods: Fine-tuning pre-trained text-to-video diffusion models at feature level by comparing spatio-temporal motion features instead of pixel-level objectives to address motion customization from reference videos.

Key Findings: Efficacy of motion customization in T2V models; ability to accurately capture complex motion and avoid content leakage from reference videos.
Spoken Stereoset: On Evaluating Social Bias Toward Speaker in Speech Large Language Models

Authors: Yi-Cheng Lin, Wei-Chih Chen, Hung-yi Lee

Year: 2024

Published in: ArXiv

Institution: National Taiwan University

Research Area: Speech LLM, Social Bias, Evaluation

Discipline: Artificial Intelligence