Personality Without Persons? A Psychometric Critique of Big Five Testing in Large Language Models
Kim ZierahnCristina CacheroAnna KorhonenNuria Oliver
Sep 2026
Human-computer Interaction
Abstract
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.
Agile - denoting "the quality of being agile, readiness for motion, nimbleness, activity, dexterity in motion" - software development methods are attempting to offer an answer to the eager business community asking for lighter weight along with faster and nimbler software development processes. This is especially the case with the rapidly growing and volatile Internet software industry as well as for the emerging mobile application environment. The new agile methods have evoked substantial amount of literature and debates. However, academic research on the subject is still scarce, as most of existing publications are written by practitioners or consultants. The aim of this publication is to begin filling this gap by systematically reviewing the existing literature on agile software development methodologies. This publication has three purposes. First, it proposes a definition and a classification of agile software development approaches. Second, it analyses ten software development methods that can be characterized as being "agile" against the defined criterion. Third, it compares these methods and highlights their similarities and differences. Based on this analysis, future research needs are identified and discussed.
P. Abrahamsson, O. Salo, Jussi Ronkainen et al.· arXiv.org· 728 citations· ⚡54
Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present a unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.
Nicolò Paternoster, Carmine Giardino, M. Unterkalmsteiner et al.· Information and Software Tec...· 394 citations· ⚡54
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
