3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.CL2025
The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks
Joan C. Timoneda
Encoder-decoder Large Language Models (LLMs), such as BERT and RoBERTa, require that all categories in an annotation task be sufficiently represented in the training data for optim…
cs.CL2025
Memory Is All You Need: Testing How Model Memory Affects LLM Performance in Annotation Tasks
Joan C. Timoneda, Sebastián Vallejo Vera
Generative Large Language Models (LLMs) have shown promising results in text annotation using zero-shot and few-shot learning. Yet these approaches do not allow the model to retain…
cs.CL2024★ 3 cited
Identifying the sources of ideological bias in GPT models through linguistic variation in output
Christina Walker, Joan C. Timoneda
Extant work shows that generative AI models such as GPT-3.5 and 4 perpetuate social stereotypes and biases. One concerning but less explored source of bias is ideology. Do GPT mode…