Behavioral Fingerprinting of Large Language Models
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 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
68be5b08c123124955ef60bb
Zehua Pei
68be5b08c123124955ef60bc
Hui-Ling Zhen
68be5b08c123124955ef60bd
Ying Zhang
68be5b08c123124955ef60be
Zhiyuan Yang
68be5b08c123124955ef60bf
Xing Li
68be5b08c123124955ef60c0
Xianzhi Yu
68be5b08c123124955ef60c1
Mingxuan Yuan
68be5b08c123124955ef60c2
Bei Yu
Behavioral Fingerprinting of Large Language Models
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 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
A Behavioral Fingerprinting framework evaluates Large Language Models using a Diagnostic Prompt Suite and automated pipeline, revealing divergent alignment behaviors and clustering patterns.
Behavioral Fingerprinting Diagnostic Prompt Suite automated evaluation pipeline Large Language Models abstract reasoning causal reasoning sycophancy semantic robustness default persona clustering ISTJ/ESTJ alignment strategies