2 papers
cs.LG2024
Improving LLM Group Fairness on Tabular Data via In-Context Learning
Valeriia Cherepanova, Chia-Jung Lee, Nil-Jana Akpinar +4
Large language models (LLMs) have been shown to be effective on tabular prediction tasks in the low-data regime, leveraging their internal knowledge and ability to learn from instr…
cs.CL2024
Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs
Valeriia Cherepanova, James Zou
Large language models (LLMs) exhibit excellent ability to understand human languages, but do they also understand their own language that appears gibberish to us? In this work we d…