34 citations · 79 across the 12 of their papers we have counts for
12 papers
Arbitration Failure, Not Perceptual Blindness: How Vision-Language Models Resolve Visual-Linguistic Conflicts
Farhad Nooralahzadeh, Omid Rohanian, Yi Zhang +2
When a Vision-Language Model (VLM) sees a blue banana and answers "yellow", is the problem of perception or arbitration? We explore the question in ten VLMs with various sizes and…
Large Language Models in the Clinic: A Comprehensive Benchmark
Fenglin Liu, Zheng Li, Hongjian Zhou +16
The adoption of large language models (LLMs) to assist clinicians has attracted remarkable attention. Existing works mainly adopt the close-ended question-answering (QA) task with…
Efficiency at Scale: Investigating the Performance of Diminutive Language Models in Clinical Tasks
Niall Taylor, Upamanyu Ghose, Omid Rohanian +4
The entry of large language models (LLMs) into research and commercial spaces has led to a trend of ever-larger models, with initial promises of generalisability, followed by a wid…
Exploring the Effectiveness of Instruction Tuning in Biomedical Language Processing
Omid Rohanian, Mohammadmahdi Nouriborji, David A. Clifton
Large Language Models (LLMs), particularly those similar to ChatGPT, have significantly influenced the field of Natural Language Processing (NLP). While these models excel in gener…
Lightweight Transformers for Clinical Natural Language Processing
Omid Rohanian, Mohammadmahdi Nouriborji, Hannah Jauncey +5
Specialised pre-trained language models are becoming more frequent in NLP since they can potentially outperform models trained on generic texts. BioBERT and BioClinicalBERT are two…
Using Bottleneck Adapters to Identify Cancer in Clinical Notes under Low-Resource Constraints
Omid Rohanian, Hannah Jauncey, Mohammadmahdi Nouriborji +6
Processing information locked within clinical health records is a challenging task that remains an active area of research in biomedical NLP. In this work, we evaluate a broad set…
MiniALBERT: Model Distillation via Parameter-Efficient Recursive Transformers
Mohammadmahdi Nouriborji, Omid Rohanian, Samaneh Kouchaki +1
Pre-trained Language Models (LMs) have become an integral part of Natural Language Processing (NLP) in recent years, due to their superior performance in downstream applications. I…
On the Effectiveness of Compact Biomedical Transformers
Omid Rohanian, Mohammadmahdi Nouriborji, Samaneh Kouchaki +1
Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks. Many existing pre-trained models, on the o…
Nowruz at SemEval-2022 Task 7: Tackling Cloze Tests with Transformers and Ordinal Regression
Mohammadmahdi Nouriborji, Omid Rohanian, David Clifton
This paper outlines the system using which team Nowruz participated in SemEval 2022 Task 7 Identifying Plausible Clarifications of Implicit and Underspecified Phrases for both subt…
Privacy-aware Early Detection of COVID-19 through Adversarial Training
Omid Rohanian, Samaneh Kouchaki, Andrew Soltan +4
Early detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational stra…
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
Omid Rohanian, Shiva Taslimipoor, Samaneh Kouchaki +2
We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically interpretable language-independent deep learning architecture. We specifically target discontin…
SHOMA at Parseme Shared Task on Automatic Identification of VMWEs: Neural Multiword Expression Tagging with High Generalisation
Shiva Taslimipoor, Omid Rohanian
This paper presents a language-independent deep learning architecture adapted to the task of multiword expression (MWE) identification. We employ a neural architecture comprising o…