Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Laradji, Issam H."'
Autor:
Sahu, Gaurav, Laradji, Issam H.
Low-resource extractive text summarization is a vital but heavily underexplored area of research. Prior literature either focuses on abstractive text summarization or prompts a large language model (LLM) like GPT-3 directly to generate summaries. In
Externí odkaz:
http://arxiv.org/abs/2407.07341
Training large language models (LLMs) for pretraining or adapting to new tasks and domains has become increasingly critical as their applications expand. However, as the model and the data sizes grow, the training process presents significant memory
Externí odkaz:
http://arxiv.org/abs/2406.17296
Autor:
Drouin, Alexandre, Gasse, Maxime, Caccia, Massimo, Laradji, Issam H., Del Verme, Manuel, Marty, Tom, Boisvert, Léo, Thakkar, Megh, Cappart, Quentin, Vazquez, David, Chapados, Nicolas, Lacoste, Alexandre
We study the use of large language model-based agents for interacting with software via web browsers. Unlike prior work, we focus on measuring the agents' ability to perform tasks that span the typical daily work of knowledge workers utilizing enterp
Externí odkaz:
http://arxiv.org/abs/2403.07718
Conducting literature reviews for scientific papers is essential for understanding research, its limitations, and building on existing work. It is a tedious task which makes an automatic literature review generator appealing. Unfortunately, many exis
Externí odkaz:
http://arxiv.org/abs/2402.01788
Autor:
Rodriguez, Juan A., Agarwal, Shubham, Laradji, Issam H., Rodriguez, Pau, Vazquez, David, Pal, Christopher, Pedersoli, Marco
Scalable Vector Graphics (SVGs) have become integral in modern image rendering applications due to their infinite scalability in resolution, versatile usability, and editing capabilities. SVGs are particularly popular in the fields of web development
Externí odkaz:
http://arxiv.org/abs/2312.11556
Generating high-quality summaries for chat dialogs often requires large labeled datasets. We propose a method to efficiently use unlabeled data for extractive summarization of customer-agent dialogs. In our method, we frame summarization as a questio
Externí odkaz:
http://arxiv.org/abs/2311.11462
Semi-supervised learning (SSL) is a widely used technique in scenarios where labeled data is scarce and unlabeled data is abundant. While SSL is popular for image and text classification, it is relatively underexplored for the task of extractive text
Externí odkaz:
http://arxiv.org/abs/2311.09559
Data augmentation is a widely used technique to address the problem of text classification when there is a limited amount of training data. Recent work often tackles this problem using large language models (LLMs) like GPT3 that can generate new exam
Externí odkaz:
http://arxiv.org/abs/2310.14192
Autor:
Sahu, Gaurav, Rodriguez, Pau, Laradji, Issam H., Atighehchian, Parmida, Vazquez, David, Bahdanau, Dzmitry
Data augmentation is a widely employed technique to alleviate the problem of data scarcity. In this work, we propose a prompting-based approach to generate labelled training data for intent classification with off-the-shelf language models (LMs) such
Externí odkaz:
http://arxiv.org/abs/2204.01959
Autor:
Saleh, Alzayat, Laradji, Issam H., Lammie, Corey, Vazquez, David, Flavell, Carol A, Azghadi, Mostafa Rahimi
Health professionals extensively use Two- Dimensional (2D) Ultrasound (US) videos and images to visualize and measure internal organs for various purposes including evaluation of muscle architectural changes. US images can be used to measure abdomina
Externí odkaz:
http://arxiv.org/abs/2109.14919