8 citations · 9 across the 5 of their papers we have counts for
5 papers
Vision-Guided Iterative Refinement for Frontend Code Generation
Hannah Sansford, Derek H. C. Law, Wei Liu +3
Code generation with large language models often relies on multi-stage human-in-the-loop refinement, which is effective but very costly - particularly in domains such as frontend w…
Uncovering Locally Low-dimensional Structure in Networks by Locally Optimal Spectral Embedding
Hannah Sansford, Nick Whiteley, Patrick Rubin-Delanchy
Standard Adjacency Spectral Embedding (ASE) relies on a global low-rank assumption often incompatible with the sparse, transitive structure of real-world networks, causing local ge…
How high is `high'? Rethinking the roles of dimensionality in topological data analysis and manifold learning
Hannah Sansford, Nick Whiteley, Patrick Rubin-Delanchy
We present a generalised Hanson-Wright inequality and use it to establish new statistical insights into the geometry of data point-clouds. In the setting of a general random functi…
GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework
Hannah Sansford, Nicholas Richardson, Hermina Petric Maretic +1
Methods to evaluate Large Language Model (LLM) responses and detect inconsistencies, also known as hallucinations, with respect to the provided knowledge, are becoming increasingly…
Implications of sparsity and high triangle density for graph representation learning
Hannah Sansford, Alexander Modell, Nick Whiteley +1
Recent work has shown that sparse graphs containing many triangles cannot be reproduced using a finite-dimensional representation of the nodes, in which link probabilities are inne…