106 citations · 150 across the 13 of their papers we have counts for
5 papers · 1 filter
On the Limits of LLM Adaptability: Impact of Model-Internalized Priors on Annotation Task Performance
Etienne Casanova, Rafal Kocielnik, R. Michael Alvarez
Large Language Models (LLMs) are increasingly used for zero-shot annotation and LLM-as-a-judge tasks, yet their reliability hinges on how model-internalized priors interact with us…
ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs
Pengrui Han, Rafal Kocielnik, Adhithya Saravanan +3
Large Language models (LLMs), while powerful, exhibit harmful social biases. Debiasing is often challenging due to computational costs, data constraints, and potential degradation…
BiasTestGPT: Using ChatGPT for Social Bias Testing of Language Models
Rafal Kocielnik, Shrimai Prabhumoye, Vivian Zhang +3
Pretrained Language Models (PLMs) harbor inherent social biases that can result in harmful real-world implications. Such social biases are measured through the probability values t…
Can You Label Less by Using Out-of-Domain Data? Active & Transfer Learning with Few-shot Instructions
Rafal Kocielnik, Sara Kangaslahti, Shrimai Prabhumoye +3
Labeling social-media data for custom dimensions of toxicity and social bias is challenging and labor-intensive. Existing transfer and active learning approaches meant to reduce an…
Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases
Shrimai Prabhumoye, Rafal Kocielnik, Mohammad Shoeybi +2
Detecting social bias in text is challenging due to nuance, subjectivity, and difficulty in obtaining good quality labeled datasets at scale, especially given the evolving nature o…