VERBA: Verbalizing Model Differences Using Large Language Models

ACL ARR 2025 May Submission6186 Authors

Abstract:

In the current machine learning landscape, we face a ``model lake'' phenomenon: Given a task, there is a proliferation of trained models with similar performances despite different behavior. For model users attempting to navigate and select from the models, documentation comparing model pairs is helpful. However, for every $N$ models there could be $\mathcal{O}(N^2)$ pairwise comparisons, a number prohibitive for the model developers to manually perform pairwise comparisons and prepare documentations.

To facilitate fine-grained pairwise comparisons among models, we introduced VERBA. Our approach leverages a large language model (LLM) to generate verbalizations of model differences by sampling from the two models.

We established a protocol that evaluates the informativeness of the verbalizations via simulation. We also assembled a suite with a diverse set of commonly used machine learning models as a benchmark.

For a pair of decision tree models with up to 5\% performance difference but 20-25\% behavioral differences, VERBA effectively verbalizes their variations with up to 80\% overall accuracy. When we included the models' structural information, the verbalization's accuracy further improved to 90\%.

VERBA opens up new research avenues for improving the transparency and comparability of machine learning models in a post-hoc manner.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: natural language explanations

Contribution Types: Model analysis & interpretability, NLP engineering experiment

Languages Studied: English

Submission Number: 6186