2 citations · 3 across the 3 of their papers we have counts for
4 papers
Correcting Prompt Dependence in LLM Benchmarks: A Bayesian Hierarchical Model with Embedding-Space Clustering
Mary Llewellyn, Isobel Thornton, James Bishop +1
LLM benchmarking metrics often misstate performance and uncertainty as they rely on two assumptions that frequently do not hold in practice: (i) a sufficient number of evaluations…
Hierarchical clustering with dot products recovers hidden tree structure
Annie Gray, Alexander Modell, Patrick Rubin-Delanchy +1
In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant o…
Statistical exploration of the Manifold Hypothesis
Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy
The Manifold Hypothesis is a widely accepted tenet of Machine Learning which asserts that nominally high-dimensional data are in fact concentrated near a low-dimensional manifold,…
Matrix factorisation and the interpretation of geodesic distance
Nick Whiteley, Annie Gray, Patrick Rubin-Delanchy
Given a graph or similarity matrix, we consider the problem of recovering a notion of true distance between the nodes, and so their true positions. We show that this can be accompl…