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The Language-Based Assessment Model Library : Open Model Sharing for Independent Validation and Broader Applications

Nilsson, August H. ; Eijsbroek, Veerle C. LU ; Gu, Zhuojun LU ; Kjell, Katarina LU ; Giorgi, Salvatore ; Kotov, Roman ; Ganesan, Adithya V. ; Schwartz, H. Andrew LU and Kjell, Oscar N.E. LU orcid (2026) In Advances in Methods and Practices in Psychological Science 9(2).
Abstract

Language-based assessments (LBAs), quantitative estimates of scientific constructs based on language, have advanced methods in the psychological and social sciences for more than a decade. LBAs based on individuals’ prompted descriptions analyzed with large language models to produce scores of their psychological states and traits have shown strong convergence with the corresponding rating scales (r > .80) and have often surpassed rating scales in predicting theoretically relevant behaviors (external criteria). Despite their high validity across numerous psychological outcomes and contexts, the broader adoption of LBA models (LBAMs) has been limited. Even when made available alongside research publications, these models often remain... (More)

Language-based assessments (LBAs), quantitative estimates of scientific constructs based on language, have advanced methods in the psychological and social sciences for more than a decade. LBAs based on individuals’ prompted descriptions analyzed with large language models to produce scores of their psychological states and traits have shown strong convergence with the corresponding rating scales (r > .80) and have often surpassed rating scales in predicting theoretically relevant behaviors (external criteria). Despite their high validity across numerous psychological outcomes and contexts, the broader adoption of LBA models (LBAMs) has been limited. Even when made available alongside research publications, these models often remain inaccessible because of technical complexities, inconsistent documentation, and the absence of a standardized repository. In this tutorial, we introduce a framework targeted to social and psychological scientists for accessible sharing models with others—the Language-Based Assessment Models (L-BAM) Library—and a toolkit for easily using LBAMs via the text package in R. L-BAM covers a wide range of models for assessing mental-health disorders (e.g., depression, anxiety), well-being (e.g., satisfaction with life, harmony in life), implicit motives (need for power, affiliation, and achievement), and more. The L-BAM Library aims to increase the availability and resource efficiency of LBAs of psychological constructs while encouraging replication, independent validation, and the broad application of preexisting LBAMs.

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author
; ; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
artificial intelligence, language, language-based assessment models, open data, open materials
in
Advances in Methods and Practices in Psychological Science
volume
9
issue
2
publisher
SAGE Publications
external identifiers
  • scopus:105038837369
ISSN
2515-2459
DOI
10.1177/25152459261419036
language
English
LU publication?
yes
additional info
Publisher Copyright: © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
id
1968ba64-bd57-437c-9b4a-210b47d10981
date added to LUP
2026-07-17 13:19:46
date last changed
2026-07-17 13:20:05
@article{1968ba64-bd57-437c-9b4a-210b47d10981,
  abstract     = {{<p>Language-based assessments (LBAs), quantitative estimates of scientific constructs based on language, have advanced methods in the psychological and social sciences for more than a decade. LBAs based on individuals’ prompted descriptions analyzed with large language models to produce scores of their psychological states and traits have shown strong convergence with the corresponding rating scales (r &gt; .80) and have often surpassed rating scales in predicting theoretically relevant behaviors (external criteria). Despite their high validity across numerous psychological outcomes and contexts, the broader adoption of LBA models (LBAMs) has been limited. Even when made available alongside research publications, these models often remain inaccessible because of technical complexities, inconsistent documentation, and the absence of a standardized repository. In this tutorial, we introduce a framework targeted to social and psychological scientists for accessible sharing models with others—the Language-Based Assessment Models (L-BAM) Library—and a toolkit for easily using LBAMs via the text package in R. L-BAM covers a wide range of models for assessing mental-health disorders (e.g., depression, anxiety), well-being (e.g., satisfaction with life, harmony in life), implicit motives (need for power, affiliation, and achievement), and more. The L-BAM Library aims to increase the availability and resource efficiency of LBAs of psychological constructs while encouraging replication, independent validation, and the broad application of preexisting LBAMs.</p>}},
  author       = {{Nilsson, August H. and Eijsbroek, Veerle C. and Gu, Zhuojun and Kjell, Katarina and Giorgi, Salvatore and Kotov, Roman and Ganesan, Adithya V. and Schwartz, H. Andrew and Kjell, Oscar N.E.}},
  issn         = {{2515-2459}},
  keywords     = {{artificial intelligence; language; language-based assessment models; open data; open materials}},
  language     = {{eng}},
  month        = {{04}},
  number       = {{2}},
  publisher    = {{SAGE Publications}},
  series       = {{Advances in Methods and Practices in Psychological Science}},
  title        = {{The Language-Based Assessment Model Library : Open Model Sharing for Independent Validation and Broader Applications}},
  url          = {{http://dx.doi.org/10.1177/25152459261419036}},
  doi          = {{10.1177/25152459261419036}},
  volume       = {{9}},
  year         = {{2026}},
}