1 code implementation • 28 Feb 2024 • Akash Gupta, Ivaxi Sheth, Vyas Raina, Mark Gales, Mario Fritz
With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across many applications.
no code implementations • 27 Feb 2024 • Vyas Raina, Samson Tan, Volkan Cevher, Aditya Rawal, Sheng Zha, George Karypis
Deep learning-based Natural Language Processing (NLP) models are vulnerable to adversarial attacks, where small perturbations can cause a model to misclassify.
no code implementations • 21 Feb 2024 • Vyas Raina, Adian Liusie, Mark Gales
Large Language Models (LLMs) are powerful zero-shot assessors and are increasingly used in real-world situations such as for written exams or benchmarking systems.
no code implementations • 4 Jan 2024 • Xiaoding Lu, Zongyi Liu, Adian Liusie, Vyas Raina, Vineet Mudupalli, Yuwen Zhang, William Beauchamp
In conversational AI research, there's a noticeable trend towards developing models with a larger number of parameters, exemplified by models like ChatGPT.
1 code implementation • 12 Sep 2023 • Vyas Raina, Mark Gales
Minimum Bayes' Risk (MBR) decoding can be used to combine system outputs in a manner that encourages better alignment with the final assessment criterion.
1 code implementation • 21 Jun 2023 • Vyas Raina, Mark Gales
Adversarial attack research in natural language processing (NLP) has made significant progress in designing powerful attack methods and defence approaches.
1 code implementation • 8 Jun 2023 • Potsawee Manakul, Yassir Fathullah, Adian Liusie, Vyas Raina, Vatsal Raina, Mark Gales
In this paper, we consider the challenge of summarizing patients' medical progress notes in a limited data setting.
no code implementations • 2 May 2023 • Vyas Raina, Mark Gales
In this work, adversarial attacks for NMT systems are explored from an output perception perspective.
no code implementations • 10 Mar 2023 • Robert Irvine, Douglas Boubert, Vyas Raina, Adian Liusie, Ziyi Zhu, Vineet Mudupalli, Aliaksei Korshuk, Zongyi Liu, Fritz Cremer, Valentin Assassi, Christie-Carol Beauchamp, Xiaoding Lu, Thomas Rialan, William Beauchamp
The proposed approach uses automatic pseudo-labels collected from user interactions to train a reward model that can be used to reject low-scoring sample responses generated by the chatbot model at inference time.
1 code implementation • 30 Jan 2023 • Vyas Raina, Mark Gales
We propose a deep-learning-based detector to identify the adversarially attackable and robust samples in an unseen dataset for an unseen target model.
no code implementations • 16 Nov 2022 • Stefano Bannò, Kate M. Knill, Marco Matassoni, Vyas Raina, Mark J. F. Gales
Though the wav2vec 2. 0 based system is found to be sensitive to the nature of the response, it can be configured to yield comparable performance to systems requiring a speech transcription, and yields gains when appropriately combined with standard approaches.
no code implementations • 19 Aug 2022 • Vyas Raina, Mark Gales
When considering the application of GEC systems to automated language assessment, the aim of an adversary could be to cheat by making a small change to a grammatically incorrect input sentence that conceals the errors from a GEC system, such that no edits are found and the candidate is unjustly awarded a perfect fluency score.
1 code implementation • NAACL 2022 • Vyas Raina, Mark Gales
Many popular image adversarial detection approaches are able to identify adversarial examples from embedding feature spaces, whilst in the NLP domain existing state of the art detection approaches solely focus on input text features, without consideration of model embedding spaces.
3 code implementations • 15 Jul 2021 • Andrey Malinin, Neil Band, Ganshin, Alexander, German Chesnokov, Yarin Gal, Mark J. F. Gales, Alexey Noskov, Andrey Ploskonosov, Liudmila Prokhorenkova, Ivan Provilkov, Vatsal Raina, Vyas Raina, Roginskiy, Denis, Mariya Shmatova, Panos Tigas, Boris Yangel
However, many tasks of practical interest have different modalities, such as tabular data, audio, text, or sensor data, which offer significant challenges involving regression and discrete or continuous structured prediction.
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