2 citations · 4 across the 15 of their papers we have counts for
5 papers · 1 filter
Tokenization Matters: Navigating Data-Scarce Tokenization for Gender Inclusive Language Technologies
Anaelia Ovalle, Ninareh Mehrabi, Palash Goyal +6
Gender-inclusive NLP research has documented the harmful limitations of gender binary-centric large language models (LLM), such as the inability to correctly use gender-diverse Eng…
JAB: Joint Adversarial Prompting and Belief Augmentation
Ninareh Mehrabi, Palash Goyal, Anil Ramakrishna +6
With the recent surge of language models in different applications, attention to safety and robustness of these models has gained significant importance. Here we introduce a joint…
Prompt Risk Control: A Rigorous Framework for Responsible Deployment of Large Language Models
Thomas P. Zollo, Todd Morrill, Zhun Deng +3
The recent explosion in the capabilities of large language models has led to a wave of interest in how best to prompt a model to perform a given task. While it may be tempting to s…
On the steerability of large language models toward data-driven personas
Junyi Li, Ninareh Mehrabi, Charith Peris +5
Large language models (LLMs) are known to generate biased responses where the opinions of certain groups and populations are underrepresented. Here, we present a novel approach to…
Coordinated Replay Sample Selection for Continual Federated Learning
Jack Good, Jimit Majmudar, Christophe Dupuy +5
Continual Federated Learning (CFL) combines Federated Learning (FL), the decentralized learning of a central model on a number of client devices that may not communicate their data…