Defense against Prompt Injection Attacks via Mixture of Encodings

1 code implementation • 10 Apr 2025 • Ruiyi Zhang, David Sullivan, Kyle Jackson, Pengtao Xie, Mei Chen

Large Language Models (LLMs) have emerged as a dominant approach for a wide range of NLP tasks, with their access to external information further enhancing their capabilities.

A Framework for Automated Measurement of Responsible AI Harms in Generative AI Applications

no code implementations • 26 Oct 2023 • Ahmed Magooda, Alec Helyar, Kyle Jackson, David Sullivan, Chad Atalla, Emily Sheng, Dan Vann, Richard Edgar, Hamid Palangi, Roman Lutz, Hongliang Kong, Vincent Yun, Eslam Kamal, Federico Zarfati, Hanna Wallach, Sarah Bird, Mei Chen

We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services.

Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural Networks

1 code implementation • 24 Jan 2019 • Paul Azunre, Craig Corcoran, Numa Dhamani, Jeffrey Gleason, Garrett Honke, David Sullivan, Rebecca Ruppel, Sandeep Verma, Jonathon Morgan

Simulated data containing a set of base classes is first used to learn an initial set of weights.

AutoML Classification +2

Abstractive Tabular Dataset Summarization via Knowledge Base Semantic Embeddings

no code implementations • 4 Apr 2018 • Paul Azunre, Craig Corcoran, David Sullivan, Garrett Honke, Rebecca Ruppel, Sandeep Verma, Jonathon Morgan

This paper describes an abstractive summarization method for tabular data which employs a knowledge base semantic embedding to generate the summary.

Abstractive Text Summarization Descriptive