Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture

Computer Science > Artificial Intelligence [Submitted on 7 Apr 2026] Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture Eduardo de la Cruz Fernández, Marcelo Karanik, Sascha Ossowski As intelligent systems become more autonomous, the scientific community focuses on creating decision-making mechanisms that include ethical and moral considerations, unlike traditional utility-maximisation models. To achieve this, a key aspect is assessing how well these decisions align with human values. To this end, a promising line of research is centred on developing approaches based on Large Language Models (LLMs) to identify human values from text, whether explicit or implicit, enabling their recognition throughout. This paper introduces a LLM-based architecture to detect and quantify the intensity of human values in text, avoiding the limitations of previous approaches tied to specific value theory or complex prompt engineering. The architecture comprises three coordinated modules: one that generates structured value specifications from the foundational texts of any theoretical framework; one that labels texts using these specifications; and one that assigns graded support or resistance based on rhetorical and semantic evidence. This modular approach separates the tasks of conceptualising from detecting human values, creating a scalable and reproducible process driven by value specifications adaptable to various theories. The architecture was instantiated with multiple LLMs and evaluated using the ValueEval dataset. The experiments demonstrate good detection performance, confirming the generality of the pipeline. Comments: 8 pages, 1 figure. Published in Proceedings of the 18th International Conference on Agents and Artificial Intelligence (ICAART 2026), Volume 5 Subjects: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY) ACM classes: I.2.7; I.2.1 Cite as: arXiv:2605.27373 [cs.AI]   (or arXiv:2605.27373v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.27373 Focus to learn more Journal reference: Proc. ICAART 2026, Vol. 5, SciTePress, 2026, pp. 4096-4103 Related DOI: https://doi.org/10.5220/0014273200004052 Focus to learn more Submission history From: Eduardo De La Cruz [view email] [v1] Tue, 7 Apr 2026 11:44:58 UTC (874 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.CL cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)