9 papers
MXNorm: Reusing MXFP block scales for efficient tensor normalisation
Callum McLean, Luke Y. Prince, Alexandre Payot +2
Matrix multiplication performance has long been the major bottleneck to scaling deep learning workloads, which has stimulated the design of new accelerators that use increasingly l…
Optimal Formats for Weight Quantisation
Douglas Orr, Luka Ribar, Carlo Luschi
Weight quantisation is an essential technique for enabling efficient training and deployment of modern deep learning models. However, the recipe book of quantisation formats is lar…
UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
Dobrik Georgiev, Kheeran Naidu, Alberto Cattaneo +3
Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon often known as hallucination). Retrieval…
Ground-Truth Subgraphs for Better Training and Evaluation of Knowledge Graph Augmented LLMs
Alberto Cattaneo, Carlo Luschi, Daniel Justus
Retrieval of information from graph-structured knowledge bases represents a promising direction for improving the factuality of LLMs. While various solutions have been proposed, a…
Elucidating the Design Space of FP4 training
Robert Hu, Carlo Luschi, Paul Balanca
The increasing computational demands of foundation models have spurred research into low-precision training, with 4-bit floating-point (\texttt{FP4}) formats emerging as a frontier…
The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Models
Alberto Cattaneo, Stephen Bonner, Thomas Martynec +4
Knowledge Graph Completion has been increasingly adopted as a useful method for helping address several tasks in biomedical research, such as drug repurposing or drug-target identi…