Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Sahu Gaurav"'
Autor:
Sahu, Gaurav, Vechtomova, Olga
Artistic inspiration remains one of the least understood aspects of the creative process. It plays a crucial role in producing works that resonate deeply with audiences, but the complexity and unpredictability of aesthetic stimuli that evoke inspirat
Externí odkaz:
http://arxiv.org/abs/2410.02881
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
Autor:
Sahu, Gaurav, Puri, Abhay, Rodriguez, Juan, Abaskohi, Amirhossein, Chegini, Mohammad, Drouin, Alexandre, Taslakian, Perouz, Zantedeschi, Valentina, Lacoste, Alexandre, Vazquez, David, Chapados, Nicolas, Pal, Christopher, Mudumba, Sai Rajeswar, Laradji, Issam Hadj
Data analytics is essential for extracting valuable insights from data that can assist organizations in making effective decisions. We introduce InsightBench, a benchmark dataset with three key features. First, it consists of 100 datasets representin
Externí odkaz:
http://arxiv.org/abs/2407.06423
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:
Hebert, Liam, Sahu, Gaurav, Guo, Yuxuan, Sreenivas, Nanda Kishore, Golab, Lukasz, Cohen, Robin
We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions. In contrast to traditional comment-only methods, our approach to labelling a comment as hate speech
Externí odkaz:
http://arxiv.org/abs/2307.09312
Recent advances in deep learning research, such as transformers, have bolstered the ability for automated agents to generate creative texts similar to those that a human would write. By default, transformer decoders can only generate new text with re
Externí odkaz:
http://arxiv.org/abs/2212.09947
Autor:
Vechtomova, Olga, Sahu, Gaurav
Electronic music artists and sound designers have unique workflow practices that necessitate specialized approaches for developing music information retrieval and creativity support tools. Furthermore, electronic music instruments, such as modular sy
Externí odkaz:
http://arxiv.org/abs/2210.15638
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