Analysis and Explainability of LLMs Via Evolutionary Methods

2026-04-27

AI Summary

This work addresses the challenge of effectively deciphering the evolutionary relationships, training lineages, and critical components of large language models (LLMs). It pioneers the systematic adaptation of phylogenetic inference methods from evolutionary biology to LLM analysis, drawing an analogy between model weights as genotypes and generated text as phenotypes. By constructing unsupervised phylogenetic trees, the approach reveals the lineage structure among models. Through weight-difference analysis, phenotypic experiments, and visualization, the method successfully reconstructs the true topology of training lineages, accurately identifies the network layers and training datasets that most significantly contribute to performance, and produces interpretable evolutionary relationship maps for multiple black-box foundation models.

Abstract

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 weight layers according to weight differences and show through phenotypic experiments that one training dataset appears to contribute more useful information than the others. Finally, we generate an unsupervised evolutionary tree of black-box foundation models. Throughout, we provide visualizations that support a clearer understanding of evolutionary relationships among LLMs.

Problem

• large language models
• explainability
• evolutionary analysis
• model relationships
• neural networks

Innovation

• evolutionary methods
• large language models
• model explainability
• genotype-phenotype analogy
• unsupervised evolutionary tree