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Found 6 papers, 2 papers with code
The FAIIR Tool: A Conversational AI Agent Assistant for Youth Mental Health Service Provision
World's healthcare systems and mental health agencies face both a growing demand for youth mental health services, alongside a simultaneous challenge of limited resources.
Calibration Attacks: A Comprehensive Study of Adversarial Attacks on Model Confidence
In this work, we highlight and perform a comprehensive study on calibration attacks, a form of adversarial attacks that aim to trap victim models to be heavily miscalibrated without altering their predicted labels, hence endangering the trustworthiness of the models and follow-up decision making based on their confidence.
Effectiveness of Data Augmentation for Parameter Efficient Tuning with Limited Data
In this paper, we examine the effectiveness of several popular task-agnostic data augmentation techniques, i. e., EDA, Back Translation, and Mixup, when using two general parameter efficient tuning methods, P-tuning v2 and LoRA, under data scarcity.
Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Support
Building Agent Assistants that can help improve customer service support requires inputs from industry users and their customers, as well as knowledge about state-of-the-art Natural Language Processing (NLP) technology.
How Curriculum Learning Impacts Model Calibration
Our studies suggest the following: most of the time curriculum learning has a negligible effect on calibration, but in certain cases under the context of limited training time and noisy data, curriculum learning can substantially reduce calibration error in a manner that cannot be explained by dynamically sampling the dataset.
SemEval-2020 Task 5: Counterfactual Recognition
Subtask-1 aims to determine whether a given sentence is a counterfactual statement or not.
