no code implementations • 8 Dec 2024 • Derek Palmer, Yifan Zhu, Kenneth Lai, Hannah VanderHoeven, Mariah Bradford, Ibrahim Khebour, Carlos Mabrey, Jack FitzGerald, Nikhil Krishnaswamy, Martha Palmer, James Pustejovsky
Our goal is to develop an AI Partner that can provide support for group problem solving and social dynamics.
no code implementations • 9 Oct 2024 • Neal Lawton, Aishwarya Padmakumar, Judith Gaspers, Jack FitzGerald, Anoop Kumar, Greg Ver Steeg, Aram Galstyan
In this paper we introduce QuAILoRA, a quantization-aware initialization for LoRA that mitigates this negative impact by decreasing quantization errors at initialization.
no code implementations • 7 Jun 2024 • Dongkyu Lee, Chandana Satya Prakash, Jack FitzGerald, Jens Lehmann
Leveraging external knowledge is crucial for achieving high performance in knowledge-intensive tasks, such as question answering.
1 code implementation • 19 May 2023 • Mustafa Safa Ozdayi, Charith Peris, Jack FitzGerald, Christophe Dupuy, Jimit Majmudar, Haidar Khan, Rahil Parikh, Rahul Gupta
We present a novel approach which uses prompt-tuning to control the extraction rates of memorized content in LLMs.
no code implementations • 13 Dec 2022 • Christopher Hench, Charith Peris, Jack FitzGerald, Kay Rottmann
Despite recent progress in Natural Language Understanding (NLU), the creation of multilingual NLU systems remains a challenge.
1 code implementation • 2 Aug 2022 • Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, Rahul Gupta, Wael Hamza, Haidar Khan, Charith Peris, Stephen Rawls, Andy Rosenbaum, Anna Rumshisky, Chandana Satya Prakash, Mukund Sridhar, Fabian Triefenbach, Apurv Verma, Gokhan Tur, Prem Natarajan
In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks.
Ranked #14 on
Natural Language Inference
on CommitmentBank
no code implementations • 15 Jun 2022 • Jack FitzGerald, Shankar Ananthakrishnan, Konstantine Arkoudas, Davide Bernardi, Abhishek Bhagia, Claudio Delli Bovi, Jin Cao, Rakesh Chada, Amit Chauhan, Luoxin Chen, Anurag Dwarakanath, Satyam Dwivedi, Turan Gojayev, Karthik Gopalakrishnan, Thomas Gueudre, Dilek Hakkani-Tur, Wael Hamza, Jonathan Hueser, Kevin Martin Jose, Haidar Khan, Beiye Liu, Jianhua Lu, Alessandro Manzotti, Pradeep Natarajan, Karolina Owczarzak, Gokmen Oz, Enrico Palumbo, Charith Peris, Chandana Satya Prakash, Stephen Rawls, Andy Rosenbaum, Anjali Shenoy, Saleh Soltan, Mukund Harakere Sridhar, Liz Tan, Fabian Triefenbach, Pan Wei, Haiyang Yu, Shuai Zheng, Gokhan Tur, Prem Natarajan
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9. 3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system.
Cross-Lingual Natural Language Inference
intent-classification
+5
6 code implementations • 18 Apr 2022 • Jack FitzGerald, Christopher Hench, Charith Peris, Scott Mackie, Kay Rottmann, Ana Sanchez, Aaron Nash, Liam Urbach, Vishesh Kakarala, Richa Singh, Swetha Ranganath, Laurie Crist, Misha Britan, Wouter Leeuwis, Gokhan Tur, Prem Natarajan
We present the MASSIVE dataset--Multilingual Amazon Slu resource package (SLURP) for Slot-filling, Intent classification, and Virtual assistant Evaluation.
Ranked #1 on
Slot Filling
on MASSIVE
1 code implementation • Asian Chapter of the Association for Computational Linguistics 2020 • Jack FitzGerald
When no translation is performed, mBART{'}s performance is comparable to the current state of the art system (Cross-Lingual BERT by Xu et al. (2020)) for the languages tested, with better average intent classification accuracy (96. 07{\%} versus 95. 50{\%}) but worse average slot F1 (89. 87{\%} versus 90. 81{\%}).