LabelHub: AI-assisted data labeling and review platform.

Assessing the reliability of LLM annotations in the context of demographic bias and model explanation (GeBNLP @ ACL 2025)

My internship annotation loop to analyze a set of horror fiction novels (including ~60 Stephen King novels and ~50 canonical horror novels, with works by H.P. Lovecraft, Ann Radcliffe and Edgar Allan Poe)

Official repository for our paper accepted at CogSci 2026: Measuring Cognitive Engagement in Collaborative Discourse with an Extended ICAP Framework: Comparing Human Annotation, In-Context Learning, and Reflective LLM Agents

NLP research project on contemporary romantic fiction, combining BERTopic, OCTIS, and Goodreads ratings to model which themes drive higher book scores.

Materials for the paper "Large Language Models Can Extract Metadata for Annotation of Human Neuroimaging Publications"

Reproducible R workflow for LLM-assisted Economic Threat and Economic Benefit annotation in German immigration news.

A dataset-auditing pipeline that flags and reviews label-quality issues across three sentiment classification datasets: the human-annotated SST-2 benchmark, and two LLM-generated synthetic datasets