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Ahmed Abdulaal

5 accepted papers

2025

Balancing Act: Diversity and Consistency in Large Language Model Ensembles

Ahmed Abdulaal, Chen Jin, Nina Montaña-Brown, Aryo Pradipta Gema, Daniel C. Castro, Daniel C. Alexander +4
ICLR 2025poster

Ensembling strategies for Large Language Models (LLMs) have demonstrated significant potential in improving performance across various tasks by combining the strengths of individual models. However, identifying the most effective ensembling method remains an open challenge, as neither maximizing out…

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2025

DeCoRe: Decoding by Contrasting Retrieval Heads to Mitigate Hallucinations

Aryo Pradipta Gema, Chen Jin, Ahmed Abdulaal, Tom Diethe, Philip Alexander Teare, Beatrice Alex +2
EMNLP 2025

Large Language Models (LLMs) often hallucinate, producing unfaithful or factually incorrect outputs by misrepresenting the provided context or incorrectly recalling internal knowledge. Recent studies have identified specific attention heads within the Transformer architecture, known as retrieval hea

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2024

Causal Modelling Agents: Causal Graph Discovery through Synergising Metadata- and Data-driven Reasoning

Ahmed Abdulaal, adamos hadjivasiliou, Nina Montana-Brown, Tiantian He, Ayodeji Ijishakin, Ivana Drobnjak +2
ICLR 2024poster

Scientific discovery hinges on the effective integration of metadata, which refers to a set of 'cognitive' operations such as determining what information is relevant for inquiry, and data, which encompasses physical operations such as observation and experimentation. This paper introduces the Causa…

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2023

SARAMIS: Simulation Assets for Robotic Assisted and Minimally Invasive Surgery

Nina Montana-Brown, Shaheer U. Saeed, Ahmed Abdulaal, Thomas Dowrick, Yakup Kilic, Sophie Wilkinson +10
NeurIPS 2023poster

Minimally-invasive surgery (MIS) and robot-assisted minimally invasive (RAMIS) surgery offer well-documented benefits to patients such as reduced post-operative pain and shorter hospital stays. However, the automation of MIS and RAMIS through the use of AI has been slow due to difficulties in data a…

Cited by 5Source ↗PDF ↗code ↗Scholar ↗
2021

Real-Time Synchronization in Neural Networks for Multivariate Time Series Anomaly Detection

Ahmed Abdulaal, Tomer Lancewicki
ICASSP 2021accepted

Deep learning has gained momentum over traditional methods in recent years due to its ability to scale up to an unforeseen rise in both volumes and dimensions of data emerging from IoT. It is suitable for modeling arbitrary complex dependencies, such as those exacerbated by asynchrony in the inputs.…

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