Issam Laradji

Beyond Trivial Counterfactual Generations with Diverse Valuable Explanations

2021

Explainability of machine learning models has gained considerable attention within our research community given the importance of deploying more reliable machine-learning systems. Explanability can also be helpful for model debugging. In computer vision applications, most …

Data Augmentation for Intent Classification with Off-the-shelf Large Language Models

2022 · arXiv (Cornell University)

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) …

LLM aided semi-supervision for Extractive Dialog Summarization

2023 · arXiv (Cornell University)

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 question-answering …