no code implementations • 23 Oct 2023 • Sabri Boughorbel, Majd Hawasly
While significant progress has been made in benchmarking Large Language Models (LLMs) across various tasks, there is a lack of comprehensive evaluation of their abilities in responding to multi-turn instructions in less-commonly tested languages like Arabic.
1 code implementation • 20 Aug 2023 • Majd Hawasly, Fahim Dalvi, Nadir Durrani
Despite the revolution caused by deep NLP models, they remain black boxes, necessitating research to understand their decision-making processes.
1 code implementation • 9 Aug 2023 • Fahim Dalvi, Maram Hasanain, Sabri Boughorbel, Basel Mousi, Samir Abdaljalil, Nizi Nazar, Ahmed Abdelali, Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Ali, Majd Hawasly, Nadir Durrani, Firoj Alam
In this study, we introduce the LLMeBench framework, which can be seamlessly customized to evaluate LLMs for any NLP task, regardless of language.
no code implementations • 24 May 2023 • Ahmed Abdelali, Hamdy Mubarak, Shammur Absar Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Yousseif Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, Firoj Alam
Our findings provide valuable insights into the applicability of LLMs for Arabic NLP and speech processing tasks.
no code implementations • 22 Jun 2022 • Aravinda Ramakrishnan Srinivasan, Yi-Shin Lin, Morris Antonello, Anthony Knittel, Mohamed Hasan, Majd Hawasly, John Redford, Subramanian Ramamoorthy, Matteo Leonetti, Jac Billington, Richard Romano, Gustav Markkula
Even though the models' RMSE value differed, all the models captured the kinematic-dependent merging behavior but struggled at varying degrees to capture the more nuanced courtesy lane change and highway lane change behavior.
no code implementations • 1 Nov 2020 • Henry Pulver, Francisco Eiras, Ludovico Carozza, Majd Hawasly, Stefano V. Albrecht, Subramanian Ramamoorthy
In this paper, we present PILOT -- a planning framework that comprises an imitation neural network followed by an efficient optimiser that actively rectifies the network's plan, guaranteeing fulfilment of safety and comfort requirements.
1 code implementation • 7 Jan 2020 • Edward Ayers, Francisco Eiras, Majd Hawasly, Iain Whiteside
Deep Neural Networks (DNNs) are finding important applications in safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the environment correctly and robustly is necessary for safe operation.
no code implementations • 11 Sep 2017 • Jawad Tayyub, Majd Hawasly, David C. Hogg, Anthony G. Cohn
This paper introduces a novel activity dataset which exhibits real-life and diverse scenarios of complex, temporally-extended human activities and actions.
no code implementations • WS 2017 • Muhannad Alomari, Paul Duckworth, Majd Hawasly, David C. Hogg, Anthony G. Cohn
This is achieved by first learning a set of visual {`}concepts{'} that abstract the visual feature spaces into concepts that have human-level meaning.
no code implementations • 25 Jul 2016 • Majd Hawasly, Florian T. Pokorny, Subramanian Ramamoorthy
Autonomous robots operating in dynamic environments must maintain beliefs over a hypothesis space that is rich enough to represent the activities of interest at different scales.
no code implementations • 1 May 2015 • Benjamin Rosman, Majd Hawasly, Subramanian Ramamoorthy
We formalise the problem of policy reuse, and present an algorithm for efficiently responding to a novel task instance by reusing a policy from the library of existing policies, where the choice is based on observed 'signals' which correlate to policy performance.
no code implementations • 15 Nov 2013 • M. M. Hassan Mahmud, Majd Hawasly, Benjamin Rosman, Subramanian Ramamoorthy
The source subset forms an `$\epsilon$-net' over the original set of MDPs, in the sense that for each previous MDP $M_p$, there is a source $M^s$ whose optimal policy has $<\epsilon$ regret in $M_p$.