6 citations · 6 across the 15 of their papers we have counts for
7 papers · 1 filter
Query-efficient model evaluation using cached responses
Hayden Helm, Ben Johnson, Carey Priebe
Evaluating a new model on an existing benchmark is often necessary to understand its behavior before deployment. For modern evaluation frameworks, generating and evaluating a respo…
Black-box model classification under the discriminative factorization
Hayden Helm, Merrick Ohata, Carey Priebe
Access to modern generative systems is often restricted to querying an API (the ``black-box" setting) and many properties of the system are unknown to the user at inference time. W…
SIGMA: Scalable Spectral Insights for LLM Model Collapse
Yi Gu, Lingyou Pang, Xiangkun Ye +4
The rapid adoption of synthetic data for training Large Language Models (LLMs) has introduced the technical challenge of "model collapse"-a degenerative process where recursive tra…
Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning
Shiyu Chen, Cencheng Shen, Youngser Park +1
Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks. However, their performance typically depends on random or min…
LLM Web Dynamics: Tracing Model Collapse in a Network of LLMs
Tianyu Wang, Akira Horiguchi, Lingyou Pang +1
The increasing use of synthetic data from the public Internet has enhanced data usage efficiency in large language model (LLM) training. However, the potential threat of model coll…
Statistical inference on black-box generative models in the data kernel perspective space
Hayden Helm, Aranyak Acharyya, Brandon Duderstadt +2
Generative models are capable of producing human-expert level content across a variety of topics and domains. As the impact of generative models grows, it is necessary to develop s…