Analysis and Explainability of LLMs Via Evolutionary Methods
Evolutionary methods have long been useful for analysis and explanation in genetics, biology, ecology, and related fields. In this work, we extend these methods to neural networks, specifically large language models (LLMs), to better analyze and explain relationships among models. We show how relating weights to genotypes and output text to phenotypes can improve our understanding of model lineage, important datasets, the roles of different model layers, and visualization of model relationships. We demonstrate this in a controlled experiment, where our estimated evolutionary trees reliably recover the topology of the ground-truth training tree. We further identify the most important weigh...
| Authors | Shannon K. Gallagher, Swati Rallapalli, Tyler Brooks et al. |
|---|---|
| Primary category | cs.NE |
| Published | 27 April 2026 |
| Last updated at source | 27 April 2026 |
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