18 citations · 39 across the 8 of their papers we have counts for
8 papers
UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
Dobrik Georgiev, Kheeran Naidu, Kheeran K. Naidu +4
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…
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…
BESS: Balanced Entity Sampling and Sharing for Large-Scale Knowledge Graph Completion
Alberto Cattaneo, Daniel Justus, Harry Mellor +7
We present the award-winning submission to the WikiKG90Mv2 track of OGB-LSC@NeurIPS 2022. The task is link-prediction on the large-scale knowledge graph WikiKG90Mv2, consisting of…
8-bit Numerical Formats for Deep Neural Networks
Badreddine Noune, Philip Jones, Daniel Justus +2
Given the current trend of increasing size and complexity of machine learning architectures, it has become of critical importance to identify new approaches to improve the computat…
Towards Structured Dynamic Sparse Pre-Training of BERT
Anastasia Dietrich, Frithjof Gressmann, Douglas Orr +3
Identifying algorithms for computational efficient unsupervised training of large language models is an important and active area of research. In this work, we develop and study a…