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

Eduardo de la Cruz Fern\'andez; Marcelo Karanik; Sascha Ossowski

REVIEW 2 major objections 4 minor 7 references

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

T0 review · 2 major / 4 minor · reviewed 2026-07-13 · grok-4.5

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Pith's one-line read A three-module LLM pipeline turns any value theory into structured specs that detect and rate values in text without theory-specific re-engineering.

desk verdict Clean modular systems paper with a real intensity scale and multi-LLM evidence; the theory-agnostic claim is only half-tested because intensity is unmeasured and VCM is never ablated. read the letter →

arxiv 2605.27373 v1 pith:XHNZSSZW submitted 2026-04-07 cs.AI cs.CLcs.CY

Eduardo de la Cruz Fernández , Marcelo Karanik , Sascha Ossowski This is my paper · ORCID

classification cs.AIcs.CLcs.CY

keywords ValueEngineeringHumanValuesDetectionLargeLanguageModelsNaturalProcessingAlignmentModularArchitectureIntensityScale

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Autonomous systems need more than utility maximisation; they need to know which human values an action promotes or demotes. This paper argues that the right way to get that signal is not a clever one-shot prompt or a model fine-tuned to one theory, but a modular pipeline that first reads the foundational texts of any value theory and turns them into machine-readable specifications. Those specifications then drive two further LLM stages: one that labels a text with the values it expresses (explicit or implicit) and one that rates each value on a graded intensity scale of support, resistance or reframing. Because conceptualisation is separated from detection, the same architecture can be pointed at Schwartz, Moral Foundations or any other framework without rewriting the detector. Instantiated with several open models and tested on the ValueEval corpus, the pipeline yields micro-F1 scores that cluster tightly across models and match classical multi-label baselines, showing that the architectural design, not the choice of a particular LLM, is what carries the performance.

What carries the argument

The Value Conceptualisation Module (VCM) that converts foundational academic texts into JSON value specifications (names, descriptions, tags, examples) which then become the sole theory-specific input to the detection and intensity stages.

What would settle it

Run the identical pipeline on a value theory whose foundational texts were never seen by any of the detector LLMs during pre-training and check whether micro-F1 remains competitive with the Schwartz/ValueEval numbers; a sharp drop would falsify the claim that the extracted specifications alone carry the theory.

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Extended reading notes

Core claim

A three-module LLM architecture that first extracts structured value specifications from the foundational texts of any theory, then uses those specifications to label values in free text and finally assigns graded intensities of support or resistance, achieves competitive multi-label detection on ValueEval while remaining essentially independent of which high-end open LLM is used, thereby confirming that modularity and theory-agnostic specifications are sufficient for general value detection.

Load-bearing premise

That the automatically extracted JSON specifications are faithful and complete enough that later detection performance can be credited to the architecture rather than to residual theory knowledge already inside the detector models.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The useful takeaway is a three-module pipeline (VCM that turns theory PDFs into JSON specs, VDM that detects then rates intensity, UIM for inspection) plus a seven-level rhetorical intensity scale. That is a cleaner separation than the authors’ own earlier Value Lens and than concurrent modular detectors such as EAVIT. They ship a public demo, run five open models under fixed temperature/seed, and show micro-F1 clustered tightly around 0.32–0.34 on a 7 600-text ValueEval subset—competitive with the contest baselines and essentially independent of which high-end LLM is used. That stability under temperature change is a genuine systems result: the prompts are tight enough that randomness barely moves the needle.

What is actually new is the automatic extraction of structured specs from arbitrary foundational texts, the explicit intensity scale with justifications, and the orchestrator that keeps conceptualisation independent of detection. The evaluation itself is honest about the numbers; they do not oversell the absolute F1.

The soft spots are real but proportional. Intensity—the second half of the claim—is shown only on a running example; no quantitative metric appears. More importantly, they never ablate or scramble the VCM JSON, never swap in a second theory, and never measure how much of the label set would still be recovered with empty specs. So the attribution of the competitive F1 to the modular, theory-agnostic design rather than residual LLM knowledge of Schwartz remains untested. That is the load-bearing gap the stress-test correctly flags. Everything else (subset size, quantisation, self-citations to prior preference work) is minor or background.

This is for people building value-aware agents or applied ethics tooling who need a reusable detector component, not for philosophers of value representation. The architecture is clear, the code is public, the detection numbers are solid enough, and the missing ablations are fixable. I would send it to referees; they will demand the intensity evaluation and a second-theory or ablation check, but the paper already earns that time.

Referee Report

2 major / 4 minor

Summary. The paper introduces a three-module LLM architecture (Value Conceptualisation Module that extracts structured JSON value specifications from foundational texts of any theory; Value Detection Module that labels presence then assigns graded intensity via a seven-level rhetorical scale; and User Interaction Module) intended to be theory-agnostic and free of heavy prompt engineering. Instantiated with several open-weight LLMs (Gemma3, Llama-4-scout, DeepSeek-R1, Qwen3, Gpt-oss) under fixed temperature/seed and Q4 quantisation, it is evaluated for multi-label presence detection on a 7 600-text subset of the Touché24-ValueEval corpus under Schwartz’s 19 values, reporting micro-F1 scores of approximately 0.32–0.34 that are essentially model-independent and claimed to be competitive with prior multi-label baselines, thereby confirming pipeline generality. Intensity scoring and justifications are illustrated only by a running example.

Significance. If the modularity and theory-agnostic claims hold, the work supplies a reusable, inspectable pipeline for value-aware autonomous systems that cleanly separates conceptualisation from detection and intensity rating—an advance over ad-hoc prompting or theory-locked classifiers. Explicit strengths include the multi-LLM consistency experiment (Table 5), temperature-robustness check (Table 6), public reference implementation, and the structured JSON intermediate representation that enables human-in-the-loop refinement. These elements make the architecture a concrete, falsifiable contribution to value engineering even if absolute F1 remains modest.

major comments (2)

[Section 4 / Tables 5–6 vs. Table 3–4] Section 4 (and Tables 5–6) evaluate only multi-label presence detection via micro-F1/precision/recall. The second half of the claimed contribution—intensity assignment by LLM3 using the seven-level scale of Table 3—is demonstrated solely by the qualitative running example of Table 4; no quantitative metrics, human agreement study, or comparison against gold intensity labels are provided. Because the abstract and §3.2 present intensity quantification as core, this omission leaves a load-bearing part of the architecture untested.
[§3.1, §4.1–4.3, Table 1] The central attribution of competitive micro-F1 to the modular, theory-agnostic design (VCM-generated JSON specs driving VDM) is not experimentally supported. There is no ablation that removes, scrambles or replaces the VCM output, no control that prompts LLM2 directly with Schwartz definitions, and no evaluation on a second value theory (e.g., Moral Foundations). Consequently it remains possible that residual pre-training knowledge of Schwartz’s 19 values, rather than the extracted specifications of §3.1/Table 1, accounts for the observed scores; the generality claim therefore rests on an untested causal assumption.

minor comments (4)

[Table 1] Table 1 is incomplete in the manuscript text (Power and Universalism rows contain only placeholder strings “Power tags / Power examples”); the full conceptualisation used for the experiments should be shown or deposited.
[§4.3] The claim that the best micro-F1 “is comparable to classical multi-label value detection baselines reported on the ValueEval contest” is left unsubstantiated; the actual baseline numbers and citation should be stated explicitly so readers can verify the comparison.
[Figure 1 / §3.4] Figure 1 is described but the flow-control numbering (1–4) is only partially explained in the prose of §3.4; a short caption clarifying each arrow would improve readability.
[throughout] Minor typographical inconsistencies appear (e.g., “Univer.(U)”, “Gpt-oss”, mixed en-dashes). A light copy-edit pass would suffice.

Circularity Check

0 steps flagged · score 1.0 of 10

Empirical systems paper with external-benchmark F1; self-citations supply background only and do not force the reported detection scores.

full rationale

The paper presents a modular LLM pipeline (VCM for theory-to-JSON specs, VDM for detection+intensity, UIM) and evaluates multi-label micro-F1/precision/recall on the external Touché24-ValueEval dataset (Schwartz labels) after stripping gold labels. Performance numbers (micro-F1 ~0.32–0.34 across five open models) are obtained by direct comparison to those held-out annotations; nothing is fitted to the evaluation set and then re-predicted. Self-citations to the authors’ prior Value Lens pipeline and preference models appear only as related-work context and as the architectural starting point being extended; they do not define the intensity scale, the evaluation metric, or the reported F1 values. No uniqueness theorem, ansatz, or definitional identity is invoked to make the results true by construction. The architecture is therefore self-contained against an independent benchmark; residual concerns about whether VCM specs (versus residual LLM knowledge of Schwartz) drive the scores are validity/ablation issues, not circularity.

Assumptions & free parameters 4 free parameters · 4 assumptions · 2 invented entities

The central claim rests on the empirical behaviour of contemporary LLMs under structured prompts, on the fidelity of automatic conceptualisation, and on the suitability of the ValueEval labels and the authors’ intensity scale. No free parameters are fitted to maximise the reported F1; the free choices are experimental controls. Invented entities are architectural modules rather than physical or mathematical objects.

free parameters (4)

temperature
Fixed at 0.0 (and ablated at 1.0) for all models; controls sampling stochasticity but is not optimised against the evaluation metric.
seed
Fixed at 42 for reproducibility; not fitted.
subset size (7 600 texts)
Hand-chosen slice of the 59 662-text ValueEval corpus for computational feasibility; selection procedure not fully specified.
quantisation (Q4_K_M)
Applied uniformly to all open-weight models; affects absolute performance but is not tuned per model.

assumptions (4)

domain assumption LLMs can extract faithful, machine-interpretable value specifications (names, tags, examples) from raw academic PDFs of a value theory without human rewriting.
Stated as the operating principle of the VCM (Section 3.1); never independently validated against expert gold specifications.
ad hoc to paper The seven-level intensity scale (+++ / + / o / – / ––– / ± / ∅) is a valid and inter-annotator-stable measure of rhetorical support or resistance.
Introduced in Table 3 and used by LLM3; no human validation or reliability study is reported.
domain assumption ValueEval multi-label annotations constitute an adequate external ground truth for Schwartz values.
Used as the sole quantitative benchmark (Section 4); accepted from the Touché24 shared task without further scrutiny.
ad hoc to paper Micro-F1 on presence detection is a sufficient proxy for the claimed end-to-end utility of the intensity-aware pipeline.
Only presence is scored; intensity is illustrated only by example.

invented entities (2)

Value Conceptualisation Module (VCM) producing JSON value specifications
purpose: To make the detector theory-agnostic by auto-generating structured definitions from source texts.
Architectural construct; no independent existence outside the proposed pipeline.
Seven-level rhetorical intensity scale with justifications
purpose: To quantify graded support or resistance rather than binary presence.
Defined ad hoc in Table 3; not derived from prior psychometrics or validated externally.

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Cite this review

Pith. "Pith review of Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture." pith.science (2026). https://pith.science/paper/XHNZSSZW

@misc{pith2026260527373,
  author       = {Pith},
  title        = {Pith review of: Identifying and Understanding Human Values in Text: A Tailorable LLM-based Architecture},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XHNZSSZW}},
  note         = {Machine review of arXiv:2605.27373}
}

read the original 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.

Figures

Figures reproduced from arXiv: 2605.27373 by the authors.

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Reference graph

Works this paper leans on

7 extracted references · 6 linked inside Pith

[1]

A., Marchesi, S., Wykowska, A., and Bel- paeme, T

Abbo, G. A., Marchesi, S., Wykowska, A., and Bel- paeme, T. (2024). Social value alignment in large language models. In Value Engineering in Arti- ficial Intelligence, pages 83–97. Springer. Bulla, L., De Giorgis, S., Mongiovì, M., and Gangemi, A. (2025). Large language models meet moral values: A comprehensive assessment. Computers in Human Behavior Repo...

arXiv 2024
[2]

ELRA. Guth, W. D. and Tagiuri, R. (1965). Personal val- ues and corporate strategy. Harvard Business Review, 43(5). Haidt, J. (2013). Moral psychology for the twenty- first century. Journal of Moral Education,

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R., Fisher, J

Hopp, F. R., Fisher, J. T., Cornell, D., Huskey, R., and Weber, R. (2021). The extended moral foun- dations dictionary (emfd). Behavior Research Methods, 53(1):232–246. Huang, S. et al. (2025). Values in the wild: Discover- ing and analyzing values in real-world language model interactions. arXiv:2504.15236. Karanik, M., Bernal, R., Peláez, J. I., and Gom...

arXiv 2021
[4]

Kennedy, B

ACM. Kennedy, B. et al. (2021). Moral concerns are dif- ferentially observable in language. Cognition, 212:104696. Khamassi, M., Nahon, M., and Chatila, R. (2024). Strong and weak alignment of large language models with human values. Scientific Reports, 14(1):19399. Kiesel, J. et al. (2022). Identifying the human values behind arguments. In Proc. ACL 2022...

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Schroter, D., Dementieva, D., and Groh, G

arXiv:2305.05335. Schroter, D., Dementieva, D., and Groh, G. (2023). Adam-smith at SemEval-2023 task

arXiv 2023
[6]

Schwartz, S

arXiv:2305.08625. Schwartz, S. H. (1992). Universals in the content and structure of values. In Adv. Exp. Soc. Psychol., volume 25, pages 1–65. Academic Press. Schwartz, S. H. et al. (2012). Refining the theory of basic individual values. J. Pers. Soc. Psychol., 103:663–88. Senthilkumar, R. A., Homayounirad, A., and Siebert, L. C. (2025). Leveraging LLMs ...

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[7]

Zhu, W., Xie, Y., Song, G., and Zhang, X

arXiv:2307.05174. Zhu, W., Xie, Y., Song, G., and Zhang, X. (2025). EA VIT: Eﬀicient and accurate human value identification from text data via llms. arXiv:2505.12792

arXiv 2025

Pith tools

Reviewed July 13, 2026 · model on record in the stance chip above.

[1] [1]

A., Marchesi, S., Wykowska, A., and Bel- paeme, T

Abbo, G. A., Marchesi, S., Wykowska, A., and Bel- paeme, T. (2024). Social value alignment in large language models. In Value Engineering in Arti- ficial Intelligence, pages 83–97. Springer. Bulla, L., De Giorgis, S., Mongiovì, M., and Gangemi, A. (2025). Large language models meet moral values: A comprehensive assessment. Computers in Human Behavior Repo...

arXiv 2024

[2] [2]

ELRA. Guth, W. D. and Tagiuri, R. (1965). Personal val- ues and corporate strategy. Harvard Business Review, 43(5). Haidt, J. (2013). Moral psychology for the twenty- first century. Journal of Moral Education,

1965

[3] [3]

R., Fisher, J

Hopp, F. R., Fisher, J. T., Cornell, D., Huskey, R., and Weber, R. (2021). The extended moral foun- dations dictionary (emfd). Behavior Research Methods, 53(1):232–246. Huang, S. et al. (2025). Values in the wild: Discover- ing and analyzing values in real-world language model interactions. arXiv:2504.15236. Karanik, M., Bernal, R., Peláez, J. I., and Gom...

arXiv 2021

[4] [4]

Kennedy, B

ACM. Kennedy, B. et al. (2021). Moral concerns are dif- ferentially observable in language. Cognition, 212:104696. Khamassi, M., Nahon, M., and Chatila, R. (2024). Strong and weak alignment of large language models with human values. Scientific Reports, 14(1):19399. Kiesel, J. et al. (2022). Identifying the human values behind arguments. In Proc. ACL 2022...

arXiv 2021

[5] [5]

Schroter, D., Dementieva, D., and Groh, G

arXiv:2305.05335. Schroter, D., Dementieva, D., and Groh, G. (2023). Adam-smith at SemEval-2023 task

arXiv 2023

[6] [6]

Schwartz, S

arXiv:2305.08625. Schwartz, S. H. (1992). Universals in the content and structure of values. In Adv. Exp. Soc. Psychol., volume 25, pages 1–65. Academic Press. Schwartz, S. H. et al. (2012). Refining the theory of basic individual values. J. Pers. Soc. Psychol., 103:663–88. Senthilkumar, R. A., Homayounirad, A., and Siebert, L. C. (2025). Leveraging LLMs ...

arXiv 1992

[7] [7]

Zhu, W., Xie, Y., Song, G., and Zhang, X

arXiv:2307.05174. Zhu, W., Xie, Y., Song, G., and Zhang, X. (2025). EA VIT: Eﬀicient and accurate human value identification from text data via llms. arXiv:2505.12792

arXiv 2025