no code implementations • 22 May 2024 • Guangzhi Sun, Potsawee Manakul, Adian Liusie, Kunat Pipatanakul, Chao Zhang, Phil Woodland, Mark Gales
Multimodal foundation models are prone to hallucination, generating outputs that either contradict the input or are not grounded by factual information.
no code implementations • 9 May 2024 • Adian Liusie, Vatsal Raina, Yassir Fathullah, Mark Gales
When Gaussian experts are used one can derive simple closed-form solutions for the optimal candidate ranking, as well as expressions for selecting which comparisons should be made to maximize the probability of this ranking.
no code implementations • 28 Mar 2024 • Piotr Molenda, Adian Liusie, Mark J. F. Gales
Watermarking generative-AI systems, such as LLMs, has gained considerable interest, driven by their enhanced capabilities across a wide range of tasks.
no code implementations • 20 Mar 2024 • Adian Liusie, Yassir Fathullah, Mark J. F. Gales
Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities and versatility in NLP tasks, however they sometimes fail to maintain crucial invariances for specific tasks.
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 • 15 Nov 2023 • Rao Ma, Adian Liusie, Mark J. F. Gales, Kate M. Knill
Text and vision foundation models can perform many tasks in a zero-shot setting, a desirable property that enables these systems to be applied in general and low-resource settings.
no code implementations • 8 Nov 2023 • Vatsal Raina, Adian Liusie, Mark Gales
Specifically, we define quality in terms of the incorrectness, plausibility and diversity of the distractor options.
no code implementations • 14 Sep 2023 • Mengjie Qian, Rao Ma, Adian Liusie, Erfan Loweimi, Kate M. Knill, Mark J. F. Gales
A key element for this process is highly rapid, flexible, search to support large archives, which in MVSE is facilitated by representing video attributes by embeddings.
1 code implementation • 10 Sep 2023 • Adian Liusie, Potsawee Manakul, Mark J. F. Gales
To address this problem, it is possible to optimise classification thresholds on a labelled data set, however, this mitigates some of the advantages of prompt-based classifiers.
1 code implementation • 15 Jul 2023 • Adian Liusie, Potsawee Manakul, Mark J. F. Gales
Current developments in large language models (LLMs) have enabled impressive zero-shot capabilities across various natural language tasks.
no code implementations • 3 Jul 2023 • Vatsal Raina, Adian Liusie, Mark Gales
Multiple-choice reading and listening comprehension tests are an important part of language assessment.
no code implementations • 22 Jun 2023 • Adian Liusie, Vatsal Raina, Andrew Mullooly, Kate Knill, Mark J. F. Gales
Multiple choice exams are widely used to assess candidates across a diverse range of domains and tasks.
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 • 9 May 2023 • Yassir Fathullah, Puria Radmard, Adian Liusie, Mark J. F. Gales
In these scenarios, where for example knowing the quality of a system's output to predict poor performance prevails over knowing the output itself, is it possible to bypass the autoregressive decoding?
3 code implementations • 15 Mar 2023 • Potsawee Manakul, Adian Liusie, Mark J. F. Gales
In this work, we propose "SelfCheckGPT", a simple sampling-based approach that can be used to fact-check the responses of black-box models in a zero-resource fashion, i. e. without an external database.
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.
2 code implementations • 28 Jan 2023 • Potsawee Manakul, Adian Liusie, Mark J. F. Gales
In this work, we introduce an alternative scheme based on standard information-theoretic measures in which the information present in the source and summary is directly compared.
1 code implementation • 13 Nov 2022 • Adian Liusie, Vatsal Raina, Mark Gales
Two metrics are described: the expected number of options, which measures whether a passage-free system can identify the answer a question using world knowledge; and the contextual mutual information, which measures the importance of context for a given question.