69 citations · 118 across the 12 of their papers we have counts for
14 papers
A Comprehensive Survey of Contamination Detection Methods in Large Language Models
Mathieu Ravaut, Bosheng Ding, Fangkai Jiao +6
With the rise of Large Language Models (LLMs) in recent years, abundant new opportunities are emerging, but also new challenges, among which contamination is quickly becoming criti…
LOCOST: State-Space Models for Long Document Abstractive Summarization
Florian Le Bronnec, Song Duong, Mathieu Ravaut +6
State-space models are a low-complexity alternative to transformers for encoding long sequences and capturing long-term dependencies. We propose LOCOST: an encoder-decoder architec…
ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?
Hailin Chen, Fangkai Jiao, Xingxuan Li +5
Upon its release in late 2022, ChatGPT has brought a seismic shift in the entire landscape of AI, both in research and commerce. Through instruction-tuning a large language model (…
On Context Utilization in Summarization with Large Language Models
Mathieu Ravaut, Aixin Sun, Nancy F. Chen +1
Large language models (LLMs) excel in abstractive summarization tasks, delivering fluent and pertinent summaries. Recent advancements have extended their capabilities to handle lon…
PromptSum: Parameter-Efficient Controllable Abstractive Summarization
Mathieu Ravaut, Hailin Chen, Ruochen Zhao +3
Prompt tuning (PT), a parameter-efficient technique that only tunes the additional prompt embeddings while keeping the backbone pre-trained language model (PLM) frozen, has shown p…
A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets
Iva Bojic, Josef Halim, Verena Suharman +6
Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality…
Unsupervised Summarization Re-ranking
Mathieu Ravaut, Shafiq Joty, Nancy Chen
With the rise of task-specific pre-training objectives, abstractive summarization models like PEGASUS offer appealing zero-shot performance on downstream summarization tasks. Howev…
Towards Summary Candidates Fusion
Mathieu Ravaut, Shafiq Joty, Nancy F. Chen
Sequence-to-sequence deep neural models fine-tuned for abstractive summarization can achieve great performance on datasets with enough human annotations. Yet, it has been shown tha…
SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
Mathieu Ravaut, Shafiq Joty, Nancy F. Chen
Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially through fine-tuning large pre-trained language models on the down…
Diabetes Mellitus Forecasting Using Population Health Data in Ontario, Canada
Mathieu Ravaut, Hamed Sadeghi, Kin Kwan Leung +2
Leveraging health administrative data (HAD) datasets for predicting the risk of chronic diseases including diabetes has gained a lot of attention in the machine learning community…
ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs
Aparna Balagopalan, Satya Gorti, Mathieu Ravaut +1
Generative Adversarial Networks (GANs) have seen steep ascension to the peak of ML research zeitgeist in recent years. Mostly catalyzed by its success in the domain of image genera…
Gradient descent revisited via an adaptive online learning rate
Mathieu Ravaut, Satya Gorti
Any gradient descent optimization requires to choose a learning rate. With deeper and deeper models, tuning that learning rate can easily become tedious and does not necessarily le…
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text
Zhe Wang, Kingsley Kuan, Mathieu Ravaut +13
The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% o…
Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge
Kingsley Kuan, Mathieu Ravaut, Gaurav Manek +7
We present a deep learning framework for computer-aided lung cancer diagnosis. Our multi-stage framework detects nodules in 3D lung CAT scans, determines if each nodule is malignan…