6 citations · 23 across the 18 of their papers we have counts for
19 papers
Soft Contextualized Encoder For User Defined Text Classification
Charu Maheshwari, Vyas Raina
User-Defined Text Classification (UDTC) considers the challenge of classifying input text to user-specified, previously unseen classes, a setting that arises frequently in real-wor…
Embedding the Teacher: Distilling vLLM Preferences for Scalable Image Retrieval
Eric He, Akash Gupta, Adian Liusie +4
Text--image retrieval is necessary for applications such as product recommendation. Embedding-based approaches like CLIP enable efficient large-scale retrieval via vector similarit…
Universal Acoustic Adversarial Attacks for Flexible Control of Speech-LLMs
Rao Ma, Mengjie Qian, Vyas Raina +2
The combination of pre-trained speech encoders with large language models has enabled the development of speech LLMs that can handle a wide range of spoken language processing task…
ZeroBench: An Impossible Visual Benchmark for Contemporary Large Multimodal Models
Jonathan Roberts, Mohammad Reza Taesiri, Ansh Sharma +31
Large Multimodal Models (LMMs) exhibit shortfalls when interpreting images and, by some measures, have poorer spatial cognition than young children or animals. Despite this, they a…
Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation Models
Vyas Raina, Mark Gales
Speech enabled foundation models, either in the form of flexible speech recognition based systems or audio-prompted large language models (LLMs), are becoming increasingly popular.…
Muting Whisper: A Universal Acoustic Adversarial Attack on Speech Foundation Models
Vyas Raina, Rao Ma, Charles McGhee +2
Recent developments in large speech foundation models like Whisper have led to their widespread use in many automatic speech recognition (ASR) applications. These systems incorpora…
LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History
Akash Gupta, Ivaxi Sheth, Vyas Raina +2
With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across…
Extreme Miscalibration and the Illusion of Adversarial Robustness
Vyas Raina, Samson Tan, Volkan Cevher +3
Deep learning-based Natural Language Processing (NLP) models are vulnerable to adversarial attacks, where small perturbations can cause a model to misclassify. Adversarial Training…
Is LLM-as-a-Judge Robust? Investigating Universal Adversarial Attacks on Zero-shot LLM Assessment
Vyas Raina, Adian Liusie, Mark Gales
Large Language Models (LLMs) are powerful zero-shot assessors used in real-world situations such as assessing written exams and benchmarking systems. Despite these critical applica…
Blending Is All You Need: Cheaper, Better Alternative to Trillion-Parameters LLM
Xiaoding Lu, Zongyi Liu, Adian Liusie +4
In conversational AI research, there's a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT. While these expansive mo…
Minimum Bayes' Risk Decoding for System Combination of Grammatical Error Correction Systems
Vyas Raina, Mark Gales
For sequence-to-sequence tasks it is challenging to combine individual system outputs. Further, there is also often a mismatch between the decoding criterion and the one used for a…
Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems
Vyas Raina, Mark Gales
With the advent of deep learning methods, Neural Machine Translation (NMT) systems have become increasingly powerful. However, deep learning based systems are susceptible to advers…
Sample Attackability in Natural Language Adversarial Attacks
Vyas Raina, Mark Gales
Adversarial attack research in natural language processing (NLP) has made significant progress in designing powerful attack methods and defence approaches. However, few efforts hav…
CUED at ProbSum 2023: Hierarchical Ensemble of Summarization Models
Potsawee Manakul, Yassir Fathullah, Adian Liusie +3
In this paper, we consider the challenge of summarizing patients' medical progress notes in a limited data setting. For the Problem List Summarization (shared task 1A) at the BioNL…
Rewarding Chatbots for Real-World Engagement with Millions of Users
Robert Irvine, Douglas Boubert, Vyas Raina +11
The emergence of pretrained large language models has led to the deployment of a range of social chatbots for chitchat. Although these chatbots demonstrate language ability and flu…
Identifying Adversarially Attackable and Robust Samples
Vyas Raina, Mark Gales
Adversarial attacks insert small, imperceptible perturbations to input samples that cause large, undesired changes to the output of deep learning models. Despite extensive research…
L2 proficiency assessment using self-supervised speech representations
Stefano Bannò, Kate M. Knill, Marco Matassoni +2
There has been a growing demand for automated spoken language assessment systems in recent years. A standard pipeline for this process is to start with a speech recognition system…
Gender Bias and Universal Substitution Adversarial Attacks on Grammatical Error Correction Systems for Automated Assessment
Vyas Raina, Mark Gales
Grammatical Error Correction (GEC) systems perform a sequence-to-sequence task, where an input word sequence containing grammatical errors, is corrected for these errors by the GEC…
Residue-Based Natural Language Adversarial Attack Detection
Vyas Raina, Mark Gales
Deep learning based systems are susceptible to adversarial attacks, where a small, imperceptible change at the input alters the model prediction. However, to date the majority of t…