7 citations · 7 across the 3 of their papers we have counts for
5 papers
Beyond Context Length: Characterising the Difficulty of Long-Context Reasoning
Shiwen Qin, Cameron Barker, Henry Gouk +4
Long-context language models support ever longer context windows, and evaluations typically treat context length as the main indicator of difficulty. However, context length alone…
Heterogeneous Structured Pruning Under Resource Constraints for Hardware-Efficient Large Language Models
Cameron Barker, Andreas Paraskeva, Jan N. van Rijn +2
Training-free pruning removes weights from pretrained large language models to reduce their deployment cost. However, existing approaches typically consider only the total number o…
Benchmarking Open-Ended Multi-Agent Coordination in Language Agents
Kale-ab Abebe Tessera, Andras Szecsenyi, Cameron Barker +7
As language models are increasingly deployed as autonomous agents, they must coordinate with others over long horizons in open-ended interactive tasks. Yet existing evaluations rar…
Structured Inference with Large Language Gibbs
Sanghyeok Choi, Henry Gouk, Esmeralda S. Whitammer
The knowledge encoded in large language models (LLMs) can serve as a substrate for structured reasoning over variables describing a complex world, but accessing this knowledge in a…
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification
Adrian El Baz, Ihsan Ullah, Edesio Alcobaça +17
Although deep neural networks are capable of achieving performance superior to humans on various tasks, they are notorious for requiring large amounts of data and computing resourc…