no code implementations • 6 May 2024 • Damin Zhang, Yi Zhang, Geetanjali Bihani, Julia Rayz
With the impressive performance in various downstream tasks, large language models (LLMs) have been widely integrated into production pipelines, like recruitment and recommendation systems.
no code implementations • 17 Jan 2024 • Geetanjali Bihani, Julia Taylor Rayz
The advent of large language models (LLMs) has enabled significant performance gains in the field of natural language processing.
1 code implementation • 30 Apr 2023 • Geetanjali Bihani, Julia Taylor Rayz
Neural network-based decisions tend to be overconfident, where their raw outcome probabilities do not align with the true decision probabilities.
no code implementations • 12 Mar 2022 • Geetanjali Bihani, Julia Taylor Rayz
With data privacy becoming more of a necessity than a luxury in today's digital world, research on more robust models of privacy preservation and information security is on the rise.
no code implementations • 5 Dec 2021 • Geetanjali Bihani
Contextual word representations generated by language models (LMs) learn spurious associations present in the training corpora.
no code implementations • NAACL (DeeLIO) 2021 • Geetanjali Bihani, Julia Taylor Rayz
Contextual word representation models have shown massive improvements on a multitude of NLP tasks, yet their word sense disambiguation capabilities remain poorly explained.
no code implementations • 22 Apr 2021 • Geetanjali Bihani, Julia Taylor Rayz
In this work, we propose a scheme to address the ambiguity in single-intent as well as multi-intent natural language utterances by creating degree memberships over fuzzified intent classes.
no code implementations • 14 Dec 2020 • Geetanjali Bihani, Julia Taylor Rayz
Static word embeddings encode word associations, extensively utilized in downstream NLP tasks.