Exploring LLMs as a Source of Targeted Synthetic Textual Data to Minimize High Confidence Misclassifications

no code implementations • 26 Mar 2024 • Philip Lippmann, Matthijs T. J. Spaan, Jie Yang

Natural Language Processing (NLP) models optimized for predictive performance often make high confidence errors and suffer from vulnerability to adversarial and out-of-distribution data.

Data Augmentation

Red Teaming for Large Language Models At Scale: Tackling Hallucinations on Mathematics Tasks

1 code implementation • 30 Dec 2023 • Aleksander Buszydlik, Karol Dobiczek, Michał Teodor Okoń, Konrad Skublicki, Philip Lippmann, Jie Yang

We consider the problem of red teaming LLMs on elementary calculations and algebraic tasks to evaluate how various prompting techniques affect the quality of outputs.

A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities

1 code implementation • 17 Oct 2022 • Andrea Tocchetti, Lorenzo Corti, Agathe Balayn, Mireia Yurrita, Philip Lippmann, Marco Brambilla, Jie Yang

Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption.