Behavioral Fingerprinting of Large Language Models

2025-09-02

AI Summary

Current LLM benchmarking overemphasizes aggregate performance metrics, failing to capture fine-grained behavioral distinctions across models. Method: We propose the “Behavioral Fingerprint” framework, leveraging diagnostic prompt sets and a strong-LLM-driven automated evaluation pipeline to systematically characterize cognitive and interaction styles—such as compliance, semantic robustness, and sycophancy—across 18 mainstream LLMs. Contribution/Results: We construct the first multidimensional LLM behavioral fingerprint atlas. Key findings include: (i) top-tier models converge on abstract and causal reasoning but exhibit marked heterogeneity in alignment-related behaviors; (ii) developers’ alignment strategies fundamentally shape model “personality”; and (iii) default personas across models cluster strongly—predominantly into ISTJ/ESTJ-type profiles. This work shifts evaluation from static performance scoring to dynamic behavioral modeling, establishing a new foundation for informed model selection, alignment diagnostics, and controllable generation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Current benchmarks for Large Language Models (LLMs) primarily focus on performance metrics, often failing to capture the nuanced behavioral characteristics that differentiate them. This paper introduces a novel ``Behavioral Fingerprinting'' framework designed to move beyond traditional evaluation by creating a multi-faceted profile of a model's intrinsic cognitive and interactive styles. Using a curated extit{Diagnostic Prompt Suite} and an innovative, automated evaluation pipeline where a powerful LLM acts as an impartial judge, we analyze eighteen models across capability tiers. Our results reveal a critical divergence in the LLM landscape: while core capabilities like abstract and causal reasoning are converging among top models, alignment-related behaviors such as sycophancy and semantic robustness vary dramatically. We further document a cross-model default persona clustering (ISTJ/ESTJ) that likely reflects common alignment incentives. Taken together, this suggests that a model's interactive nature is not an emergent property of its scale or reasoning power, but a direct consequence of specific, and highly variable, developer alignment strategies. Our framework provides a reproducible and scalable methodology for uncovering these deep behavioral differences. Project: https://github.com/JarvisPei/Behavioral-Fingerprinting

Problem

• Analyzing nuanced behavioral characteristics of LLMs
• Moving beyond traditional performance-focused benchmarks
• Investigating divergence in alignment-related behaviors across models

Innovation

• Behavioral fingerprinting framework for nuanced evaluation
• Diagnostic prompt suite with automated LLM judge
• Analyzing alignment strategies via cross-model persona clustering