arXiv Machine Learning
Sep 4

Inferred Generative-Process Diversity Predicts Correlated Failure Across Language Models

The paper introduces a new measure of generative‑process diversity for language models, using Normalised Compression Distance on raw outputs after controlling for permutation effects. Across 38 models, this metric uncovers population structure that semantic similarity misses and predicts lower correlated failures across ten benchmark families, independent of semantic similarity or model capability. The authors argue that higher generative‑process diversity reduces correlated failures in multi‑model systems, offering a practical tool for safety‑relevant applications.

By Ross Tieman, Evan Markou
Hugging Face Trending Papers
Sep 3

Inferred Generative-Process Diversity Predicts Correlated Failure Across Language Models

The paper argues that traditional semantic similarity fails to capture the true diversity of language models. It introduces a new metric—generative‑process diversity—measured via Normalised Compression Distance on raw outputs, which reveals hidden population structure among 38 models. This metric predicts lower correlated failures across ten benchmark families, independent of semantic similarity or model capability.

arXiv Machine Learning
Aug 11

On the Effect of Sampling Diversity in Scaling LLM Inference

arXiv:2502. 11027v5 Announce Type: replace Abstract: Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhance it.

By Tianchun Wang, Zichuan Liu, Yuanzhou Chen, Jonathan Light, Weiyang Liu, Haifeng Chen, Xiang Zhang, Wei Cheng