4 citations · 14 across the 9 of their papers we have counts for
4 papers · 1 filter
The Calls are Coming from Inside the Model: Investigating Probe-based Detection of Tool-Calling Errors in LLMs
Eric Yeats, Brendan Kennedy, Loc Truong +5
The hidden states of large language models (LLMs) are known to capture rich information relating to model knowledge and behavior that can be hard to extract from examination of inp…
TAGTorch: A PyTorch Library for Geometry, Topology, and Symmetry-Aware Machine Learning
Brendan Kennedy, Tegan Emerson, Gregory Roek +2
Over the last decade, neural networks have been applied to an increasingly diverse range of applications, including data with rich geometric, topological, or symmetry-related struc…
What do Geometric Hallucination Detection Metrics Actually Measure?
Eric Yeats, John Buckheit, Sarah Scullen +9
Hallucination remains a barrier to deploying generative models in high-consequence applications. This is especially true in cases where external ground truth is not readily availab…
Directional Concentration Uncertainty: A representational approach to uncertainty quantification for generative models
Souradeep Chattopadhyay, Brendan Kennedy, Sai Munikoti +2
In the critical task of making generative models trustworthy and robust, methods for Uncertainty Quantification (UQ) have begun to show encouraging potential. However, many of thes…