8 citations · 11 across the 5 of their papers we have counts for
11 papers · 1 filter
Readable but Not Controllable: Neuron-Level Evidence for Medical LLM Hallucination
Vijay Vankadaru, Asha Matthews, Tanya Roosta +1
Hallucination remains one of the central obstacles to deploying medical LLMs. Yet, even when hallucination can be detected, it is still unclear whether the internal representations…
The Order Effect: Investigating Prompt Sensitivity to Input Order in LLMs
Bryan Guan, Tanya Roosta, Peyman Passban +1
As large language models (LLMs) become integral to diverse applications, ensuring their reliability under varying input conditions is crucial. One key issue affecting this reliabil…
What is Lost in Knowledge Distillation?
Manas Mohanty, Tanya Roosta, Peyman Passban
Deep neural networks (DNNs) have improved NLP tasks significantly, but training and maintaining such networks could be costly. Model compression techniques, such as, knowledge dist…
Training Mixed-Domain Translation Models via Federated Learning
Peyman Passban, Tanya Roosta, Rahul Gupta +2
Training mixed-domain translation models is a complex task that demands tailored architectures and costly data preparation techniques. In this work, we leverage federated learning…
Not Far Away, Not So Close: Sample Efficient Nearest Neighbour Data Augmentation via MiniMax
Ehsan Kamalloo, Mehdi Rezagholizadeh, Peyman Passban +1
In Natural Language Processing (NLP), finding data augmentation techniques that can produce high-quality human-interpretable examples has always been challenging. Recently, leverag…
Robust Embeddings Via Distributions
Kira A. Selby, Yinong Wang, Ruizhe Wang +4
Despite recent monumental advances in the field, many Natural Language Processing (NLP) models still struggle to perform adequately on noisy domains. We propose a novel probabilist…