Nuria Oliver | Search arXiv

arXiv2026

From Interpretability Methods to Interpretable Models

More than a decade in, explainable AI (XAI) for computer vision has assembled a mature toolbox: attribution, feature visualization, concept-based, and circuit-based methods. Yet almost all of the field's effort has gone into building and comparing these methods, and little into the question they were meant to answer---how interpretable are our models, and are we making progress as they evolve? We argue for shifting the field's focus from methods to models, along two complementary lines. One is already within reach: existing tools let us characterize and compare what different models represent and compute. The other is harder, and largely neglected: whether a model can actually be understood by the humans who rely on it---the independent evaluators on whom trust and certification depend, not the experts confirming what they already expect. It can only be measured, not inferred. We review why the toolbox is mature enough to support both, survey the thin body of work comparing models, draw a parallel to systems neuroscience, and close with a model-centric XAI agenda.

cs.CV↗

arXiv2026

Personality Without Persons? A Psychometric Critique of Big Five Testing in Large Language Models

Human personality inventories are increasingly used to characterize large language models (LLMs), compare systems, and inform downstream governance claims. Yet, these inventories were developed and validated for humans, and it remains unclear whether they are valid for non-human systems. We present a systematic psychometric evaluation of Big Five personality measurement in LLMs. We ask three research questions: Do Big Five inventories a) appropriately describe LLMs, b) capture meaningful differences between models, and c) reflect internal factors consistent with human personality? We assess the content validity of five candidate Big Five inventories and administer the best-performing inventory to N = 264 LLMs spanning 50 model families. Our findings are threefold. First, Big Five items adapted for LLMs achieve acceptable content validity, whereas the original human-developed items do not. Second, Big Five inventories fail to capture meaningful differences across LLMs: between-model variance accounts for only 7% - 17% of the total score variance. Third, LLMs responses do not reproduce the canonical Big Five five-factor structure of human personality, with four of the five personality facets collapsing into one (r >= .90). Moreover, comparisons between base and instruction-tuned variants suggest that alignment training shifts Big Five scores toward socially desirable profiles. These findings demonstrate that Big Five inventories do not measure a construct equivalent to human personality in LLMs. Thus, using human personality frameworks to characterize, benchmark, compare, or govern LLMs risks producing misleading conclusions. We highlight the need for evaluation frameworks that are specifically designed and validated for LLMs, rather than transferring human psychological constructs without first establishing their validity.

cs.HC↗