BehaviorBox: Automated Discovery of Fine-Grained Performance Differences Between Language Models
Year: 2025
Type: conference-paper
Abstract: Language model evaluation is a daunting task: prompts are brittle, corpus-level perplexities are vague, and the choice of benchmarks are endless.Finding examples that show meaningful, generalizable differences between two LMs is crucial to understanding where one model succeeds and another fails.Can this process be done automatically?In this work, we propose methodology for automated comparison of language models that uses performance-aware contextual embeddings to find fine-grained features of text where one LM outperforms another.Our method, which we name BEHAVIORBOX, extracts coherent features that demonstrate differences with respect to the ease of generation between two LMs.Specifically, BEHAVIORBOX finds features that describe groups of words in fine-grained contexts, such as conditional 'were' in the phrase 'if you were' and exclamation marks after emotional statements, where one model outperforms another within a particular datatset.We apply BEHAVIORBOX to compare models that vary in size, model family, and post-training, and enumerate insights into specific contexts that illustrate meaningful differences in performance which cannot be found by measures such as corpus-level perplexity alone.1
Authors: Lindia Tjuatja, Graham Neubig
Institution: Carnegie Mellon University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Amazon Web Services
OpenAlex ID: W4412944988