1 citations · 1 across the 2 of their papers we have counts for
5 papers
A Methodological Guide on Using Large Language Models for Reproducible Text Annotation in the Social Sciences and Humanities with Python and R
Qixiang Fang, Javier Garcia Bernardo, Erik-Jan van Kesteren
Large language models (LLMs) are increasingly used by researchers in the social sciences and humanities (SSH) for text analysis, particularly to automate text annotation. However,…
Improving Stance Detection by Leveraging Measurement Knowledge from Social Sciences: A Case Study of Dutch Political Tweets and Traditional Gender Role Division
Qixiang Fang, Anastasia Giachanou, Ayoub Bagheri
Stance detection concerns automatically determining the viewpoint (i.e., in favour of, against, or neutral) of a text's author towards a target. Stance detection has been applied t…
PATCH! {P}sychometrics-{A}ssis{T}ed Ben{CH}marking of Large Language Models against Human Populations: A Case Study of Proficiency in 8th Grade Mathematics
Qixiang Fang, Daniel L. Oberski, Dong Nguyen
Many existing benchmarks of large (multimodal) language models (LLMs) focus on measuring LLMs' academic proficiency, often with also an interest in comparing model performance with…
General-Purpose User Modeling with Behavioral Logs: A Snapchat Case Study
Qixiang Fang, Zhihan Zhou, Francesco Barbieri +6
Learning general-purpose user representations based on user behavioral logs is an increasingly popular user modeling approach. It benefits from easily available, privacy-friendly y…
USE: Dynamic User Modeling with Stateful Sequence Models
Zhihan Zhou, Qixiang Fang, Leonardo Neves +5
User embeddings play a crucial role in user engagement forecasting and personalized services. Recent advances in sequence modeling have sparked interest in learning user embeddings…