Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture
This paper introduces an LLM-based architecture to detect and quantify the intensity of human values in text. The architecture comprises three coordinated modules that can adapt to various value theories, and experiments on the ValueEval dataset show good detection performance.
Article intelligence
Key points
- Proposes a modular LLM architecture for identifying human values in text, avoiding dependence on specific value theories or complex prompt engineering.
- Three modules: generate structured value specifications, label texts using them, and assign graded support or resistance based on rhetorical and semantic evidence.
- Evaluated on the ValueEval dataset, demonstrating good performance and generality.
Why it matters
This matters because proposes a modular LLM architecture for identifying human values in text, avoiding dependence on specific value theories or complex prompt engineering.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27373] Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture
[Submitted on 7 Apr 2026]
Title:Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture
View a PDF of the paper titled Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture, by Eduardo de la Cruz Fern\'andez and 2 other authors
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Abstract: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
arXiv-issued DOI via DataCite
Journal reference: Proc. ICAART 2026, Vol. 5, SciTePress, 2026, pp. 4096-4103
Related DOI:
https://doi.org/10.5220/0014273200004052
DOI(s) linking to related resources
Submission history
From: Eduardo De La Cruz [view email] [v1] Tue, 7 Apr 2026 11:44:58 UTC (874 KB)
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