18 citations · 34 across the 9 of their papers we have counts for
9 papers
Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations
Shahin Atakishiyev, Housam K. B. Babiker, Jiayi Dai +8
Large language models have exhibited impressive performance across a broad range of downstream tasks in natural language processing. However, how a language model predicts the next…
StructTest: Benchmarking LLMs' Reasoning through Compositional Structured Outputs
Hailin Chen, Fangkai Jiao, Mathieu Ravaut +8
The rapid advancement of large language models (LLMs) demands robust, unbiased, and scalable evaluation methods. However, human annotations are costly to scale, model-based evaluat…
Deep Temporal Modelling of Clinical Depression through Social Media Text
Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan +1
We describe the development of a model to detect user-level clinical depression based on a user's temporal social media posts. Our model uses a Depression Symptoms Detection (DSD)…
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach
Nawshad Farruque, Randy Goebel, Sudhakar Sivapalan +1
A fundamental component of user-level social media language based clinical depression modelling is depression symptoms detection (DSD). Unfortunately, there does not exist any DSD…
A comprehensive empirical analysis on cross-domain semantic enrichment for detection of depressive language
Nawshad Farruque, Randy Goebel, Osmar Zaiane
We analyze the process of creating word embedding feature representations designed for a learning task when annotated data is scarce, for example, in depressive language detection…
STEP-EZ: Syntax Tree guided semantic ExPlanation for Explainable Zero-shot modeling of clinical depression symptoms from text
Nawshad Farruque, Randy Goebel, Osmar Zaiane +1
We focus on exploring various approaches of Zero-Shot Learning (ZSL) and their explainability for a challenging yet important supervised learning task notorious for training data s…
Basic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
Nawshad Farruque, Chenyang Huang, Osmar Zaiane +1
In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers. We choose ou…
A multi-component framework for the analysis and design of explainable artificial intelligence
S. Atakishiyev, H. Babiker, N. Farruque +6
The rapid growth of research in explainable artificial intelligence (XAI) follows on two substantial developments. First, the enormous application success of modern machine learnin…
Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation
Chenyang Huang, Amine Trabelsi, Xuebin Qin +2
Emotion detection in text is an important task in NLP and is essential in many applications. Most of the existing methods treat this task as a problem of single-label multi-class t…