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REVIEW 4 major objections 10 minor 23 references

Public Service Algorithm: towards a transparent, explainable, and scalable content curation for news content based on editorial values

T0 review · 4 major / 10 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims LLMs can approximate the collective editorial judgment of news editors when ranking articles against public-service values, with the best models matching the human top-5 list in three of five positions.

desk verdict A promising but overclaimed pilot: the headline alignment numbers are internally inconsistent and the aggregation across criteria is underspecified. read the letter →

arxiv 2506.22270 v2 pith:477S6VUG submitted 2025-06-27 cs.CY

Ahmad Mel , Sebastien Noir This is my paper
classification cs.CY
keywords largelanguagemodelscontentcurationpublicservicemediaeditorialvaluesnewsrecommendationinter-raterreliabilityNDCGtop-kselection
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

The paper tries to establish that large language models, prompted with explicit editorial criteria, can rate news articles against public-service values closely enough to reproduce the top choices of a panel of human editors. If true, a news organisation could automate the first pass of content selection—flagging diverse, in-depth, forward-looking, and cross-border stories—while keeping a human editor in charge of final judgment. The evidence is a head-to-head comparison: thirty articles from a European news exchange were rated by 27 editors and by eight LLMs on four criteria. The best models, Qwen 72B and Mistral Nemo, reached a 75% overlap with the human consensus top-5 list and NDCG@5 scores above 0.94.

What carries the argument

The Public Service Algorithm (PSA) is the central framework: a set of criterion-specific prompts, each a faithful extension of a human editorial guide, that asks an LLM to reason first and then assign a numerical score on diversity, in-depth analysis, forward-looking, and cross-border relevance. The comparison machinery is ICC(2,k) for human inter-rater reliability, NDCG@5 for rank-weighted alignment, and Precision@5 for overlap of top-5 selections. The load-bearing object is the average of human ratings, which is treated as the consensus ground truth for each article's value.

What would settle it

Re-run the comparison with a complete rating matrix in which every editor rates every article on every criterion, or use an independent ground truth such as the articles editors actually published or promoted; if the best LLM's NDCG@5 falls below roughly 0.8 or Precision@5 below 0.5, the claim of alignment with expert consensus is weakened.

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Extended reading notes

Core claim

The central claim is that LLM ratings can approximate collective editorial judgment in top-k content selection. On a 30-article multilingual sample, Qwen 72B achieves NDCG@5 of 0.9495 and Precision@5 of 0.75, while Mistral Nemo achieves 0.9421 and 0.75, meaning three of the four articles the editors ranked highest appear in each model's top five. The paper also reports that agreement is stronger on structurally defined criteria (in-depth analysis, ICC 0.942, and cross-border relevance, ICC 0.891) and weaker on more subjective ones (diversity, ICC 0.791, and forward-looking, ICC 0.747), and that LLMs mirror the human tendency to assign low scores on diversity and forward-looking.

Load-bearing premise

The average of the incomplete and inconsistent human ratings (68.9% completion, some editors rating as few as one article, and spreads of about two scale points) is treated as the true editorial value of each article, and all LLM alignment is measured against this aggregate.

Editorial extensions

If this is right

  • LLMs can serve as a scalable first-pass rater for value-driven news curation, surfacing candidate articles for final editorial review.
  • The high top-5 alignment suggests automated ranking could be used to prioritise which incoming articles editors see first in a news exchange.
  • Well-defined criteria transfer to LLMs better than subjective ones, so diversity and forward-looking ratings will need more careful prompt design or continued human oversight.
  • The same framework can preserve transparency by having LLMs output a rationale before the rating, making the automated judgment inspectable by journalists.
  • The approach opens a path toward editorially driven recommendation and threshold-based content selection, rather than engagement-optimised ranking.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the human consensus is unstable—completion was only 68.9% and some editors rated a single article—then the reported NDCG and Precision numbers measure alignment with a noisy aggregate, and a complete rating matrix might produce a different human top-5.
  • With only 30 articles, the top-5 metrics are coarse; a larger pool would test whether the alignment persists when the model must choose from hundreds or thousands of candidates.
  • A direct within-newsroom test would be to compare LLM top-5 rankings against the articles editors actually choose to publish or promote, rather than against aggregated ratings from a variable panel.
  • Reader-response data could be added to see whether articles flagged by PSA as high-value also lead to longer reading time or higher return visits, linking the editorial values to audience behaviour.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 10 minor

Summary. The paper introduces the Public Service Algorithm (PSA), a framework that uses LLMs to rate news articles against four PSM-inspired editorial criteria (In-depth Analysis, Diversity, Cross-border Relevance, Forward Looking) and compares these ratings with those of a human editorial panel on a dataset of 30 articles from the 'A European Perspective' project. The stated central claim is that LLMs can align with expert consensus in selecting top-5 content, with reported NDCG@5 values of 0.9495 (Qwen 72B) and Precision@5 of 0.75 for the best models. The paper also presents inter-rater reliability scores (ICC) for the human panel, per-criterion rating distributions, and a discussion of limitations including sparse human ratings and subjectivity in the criteria. The authors position this as a first step toward scalable, transparent, value-driven curation for public service media.

Significance. If the alignment result holds, the paper would demonstrate a practical pathway for augmenting human editorial judgment with LLMs in news content curation, addressing a real scalability problem in public service media. The study's strengths include a clear articulation of the four editorial criteria, a reproducible dataset (linked publicly), explicit acknowledgment of the limitations of the human baseline, and the use of established agreement metrics (ICC, NDCG, Precision@5). The paper also provides a useful comparison across eight diverse LLMs. However, the significance is tempered by the small sample size (30 articles), the sparse and incomplete human rating matrix (68.9% completion), the lack of uncertainty quantification, and the internal inconsistencies in the reported top-5 metrics. As a preliminary feasibility study it offers a valuable starting point, but the evidence as presented does not yet robustly support the headline claim of strong LLM-human alignment.

major comments (4)
  1. [Table 3 vs. Table 2] The reported Precision@5 values for Qwen 72B and Mistral Nemo (0.75) are inconsistent with the top-5 lists shown in Table 2. For Qwen 72B, the overlap with the human top-5 (Article 05) is four articles (24, 03, 12, 05), giving Precision@5 = 0.80; the same holds for Mistral Nemo (03, 05, 12, 24), also 0.80. Please reconcile the numbers or state explicitly how the top-5 selection for Table 3 differs from that in Table 2, or correct the table. An internal contradiction in the headline metrics prevents the reader from verifying the central claim.
  2. [Section 2.6 and Section 3.5] The aggregation rule for combining the four criterion-specific ratings into the single 'average score' used to rank articles is never specified. Section 2.6 defines NDCG@5 and Precision@5 for a single ranking, and Section 3.5 mentions 'the ranking produced by the LLM' without stating how the per-criterion scores (or per-criterion rankings) are merged into one top-5 list. State the exact combination rule (e.g., unweighted mean across the four criteria, or per-criterion NDCG averaged) and justify it. Without this, the aggregate metrics are not reproducible, and the paper's own observation (Section 3.5) that alignment is weaker on Diversity and Forward Looking suggests that an unspecified aggregate could mask important criterion-specific variation.
  3. [Section 2.4, Section 2.6, Section 3.6] The ground truth for the NDCG@5 and Precision@5 comparisons is the average of incomplete human ratings (68.9% completion, with at least one editor rating only one article), and the ICC values in Table 1 were computed after pruning raters who did not complete a 'minimal set of articles.' While these limitations are disclosed, the headline alignment result has no confidence intervals, bootstrap estimates, or sensitivity analyses with respect to the pruning threshold or the sparsity of the rating matrix. For example, a random top-5 selection has expected Precision@5 of about 0.17, so the reported 0.75 is clearly above chance, but the reader cannot judge whether the differences between models (0.75 vs. 0.60) are meaningful. The authors should report bootstrap confidence intervals for NDCG@5 and Precision@5, or at lest a leave-one-rater-out analysis, to establish the stability of the human consensus.
  4. [Section 3.5 and Section 2.6] The evaluation lacks any comparison against simple, non-LLM baselines (e.g., ranking by article length, recency, a keyword-based heuristic, or random selection). Reporting NDCG@5 and Precision@5 without such baselines gives no indication of the added value of the LLM-based PSA pipeline relative to trivial alternatives. If, for instance, a keyword-based classifier achieves comparable NDCG@5, the claim that LLMs 'can align with expert consensus' would be less compelling. Please add at least one baseline to contextualize the absolute values.
minor comments (10)
  1. [Section 1] Typo: 'Recent advances LLMs have catalyzed' should be 'Recent advances in LLMs have catalyzed'.
  2. [Section 2.1] Capitalize 'English' and rephrase 'All the languages have been translated into english and verified by human'.
  3. [Section 2.4] The text says 'a panel of 30 experienced editors' but later states 'of the 30 editors, 27 participated.' Please clarify the exact number of active raters.
  4. [Section 2.6] The pruning criterion for ICC ('ignored raters that did not complete all ratings on a minimal set of articles per criteria') is vague; specify the exact threshold for inclusion.
  5. [Section 3.1 / Section 3.4] Figure 3 is referenced in Section 3.1 as showing box plots, but the figure is only described in detail in Section 3.4. Ensure the figure numbering and placement are consistent.
  6. [Section 3.5] The phrase 'despite refining the guide with incremental guide 4' is unclear; refer explicitly to the appendix section containing the incremental scoring guidance.
  7. [Throughout] The model name is inconsistently written as 'Mistral Nemo' and 'Mistral NeMo'; use one spelling throughout.
  8. [Section 2.2 vs. References] The model list refers to 'Qwen-25.72BInstruct' and the reference [16] is 'Qwen-72B', while the table uses 'Qwen 72B'. Please align the naming to the exact model (e.g., Qwen2.5-72B-Instruct) and ensure the reference matches.
  9. [Appendix: Detailed PSA Criteria] The abbreviation 'CoE' is used without defining 'Council of Europe' at first use.
  10. [Section 4] Grammar: 'In this study we successfully demonstrates' should be 'we successfully demonstrate'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: human-editor ground truth is external, LLMs are zero-shot, and no fitted parameter or self-citation chain drives the result.

full rationale

The derivation chain is self-contained with respect to the central claim. The paper compares LLM-generated ratings against a human-editor ground truth collected from 27 AEP editors (Section 2.4); the human ratings are external to the LLMs and to the authors' model parameters. No parameter is fitted to the human labels: the LLMs are used in a zero-shot setting (Section 2.5), and the reported NDCG@5 and Precision@5 values are computed by applying standard IR metrics to independently produced rankings (Section 2.6). The shared editorial rubric and criterion-specific prompts are an operationalization of the same PSM values, but the human judgments remain independent ratings, so the comparison is empirical rather than definitional. The paper contains no load-bearing self-citation: references to prior LLM-credibility work are external, and no uniqueness theorem or prior result by the present authors is invoked to force the choice of LLM or metric. The limitations the paper itself reports—68.9% completion, sparse rating matrix, inter-rater variability, translation loss—are validity and generalizability concerns, not circularity. The internal inconsistency between Table 2's top-5 lists (4/5 overlap = 0.80 Precision@5 for Qwen 72B and Mistral Nemo) and Table 3's reported Precision@5 of 0.75 is a reproducibility or correctness issue in the manuscript, but it does not constitute a circular derivation, since the claimed alignment is still measured against an external human benchmark rather than against the models' own outputs.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a small, hand-crafted evaluation design. There are no fitted numerical constants in the main analysis, but the choice of top-k, the ICC pruning rule, and the LLM prompt texts are all researcher-chosen elements that shape the reported alignment scores. The domain assumptions listed above are necessary for the human benchmark to be meaningful.

free parameters (3)
  • Top-k selection threshold = 5
    NDCG and Precision are computed only over the top-5 ranked articles; results may change with a different k.
  • ICC pruning rule = not fully specified
    To compute ICC(2,k), raters who did not complete ratings on a minimal set of articles per criterion were omitted (Section 2.6); the exact threshold is not reported, which affects reported ICC values.
  • LLM prompt formulations = criterion-specific prompt texts in Appendix
    The prompts were hand-crafted extensions of the editorial criteria; their wording is a design choice that likely affects alignment scores and is not independently validated.
assumptions (5)
  • domain assumption LLM ratings on a 0 to 5 scale are directly comparable to human editor ratings on the same scale
    The experiment treats the numerical outputs of LLMs as if they occupy the same measurement scale as human scores (Section 2.5).
  • domain assumption The average of the available human ratings is a valid ground truth for article quality
    Used throughout Section 3 to build the human consensus top-5 list, despite incomplete and variable rater participation (Section 2.4).
  • domain assumption English translations preserve the properties relevant to the four editorial criteria
    All articles were translated to English before LLM processing; the authors note this may lose linguistic and cultural nuance (Section 3.6).
  • standard math ICC(2,k) remains valid after pruning incomplete raters
    The authors apply ICC to a pruned rating matrix, but the assumptions of a complete two-way random-effects design are violated by the pruning (Section 2.6).
  • domain assumption A single zero-shot LLM response per article is representative
    The non-deterministic nature of LLMs is acknowledged, but no repeated sampling or consistency analysis is reported (Section 3.6).

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Cite this review

Pith. "Pith review of Public Service Algorithm: towards a transparent, explainable, and scalable content curation for news content based on editorial values." pith.science (2026). https://pith.science/paper/477S6VUG

@misc{pith2026250622270,
  author       = {Pith},
  title        = {Pith review of: Public Service Algorithm: towards a transparent, explainable, and scalable content curation for news content based on editorial values},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/477S6VUG}},
  note         = {Machine review of arXiv:2506.22270}
}
read the original abstract

The proliferation of disinformation challenges traditional, unscalable editorial processes and existing automated systems that prioritize engagement over public service values. To address this, we introduce the Public Service Algorithm (PSA), a novel framework using Large Language Models (LLMs) for scalable, transparent content curation based on Public Service Media (PSM) inspired values. Utilizing a large multilingual news dataset from the 'A European Perspective' project, our experiment directly compared article ratings from a panel of experienced editors from various European PSMs, with those from several LLMs, focusing on four criteria: diversity, in-depth analysis, forward-looking, and cross-border relevance. Utilizing criterion-specific prompts, our results indicate a promising alignment between human editorial judgment and LLM assessments, demonstrating the potential of LLMs to automate value-driven curation at scale without sacrificing transparency. This research constitutes a first step towards a scalable framework for the automatic curation of trustworthy news content.

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Reference graph

Works this paper leans on

23 extracted references · 17 canonical work pages

  1. [1]

    27, 2017

    Wardle, C., and Derakhshan, H.,Information disorder: Toward an interdisciplinary framework for research and policymaking, Vol. 27, 2017

    work page 2017

  2. [2]

    Social media and the public interest: Governance of news platforms in the realm of individual and algorithmic gatekeepers,

    Napoli, P. M., “Social media and the public interest: Governance of news platforms in the realm of individual and algorithmic gatekeepers,”Telecommunications Policy, Vol. 39, No. 9, 2015, pp. 751–760

    work page 2015

  3. [3]

    Exposurediversityasadesignprincipleforrecommendersystems,

    Helberger,N.,Karppinen,K.,andD’acunto,L.,“Exposurediversityasadesignprincipleforrecommendersystems,” Information, communication & society, Vol. 21, No. 2, 2018, pp. 191–207

    work page 2018

  4. [4]

    Seeingwithoutknowing: Limitationsofthetransparencyidealanditsapplicationtoalgorithmic accountability,

    Ananny,M.,andCrawford,K.,“Seeingwithoutknowing: Limitationsofthetransparencyidealanditsapplicationtoalgorithmic accountability,”new media & society, Vol. 20, No. 3, 2018, pp. 973–989

    work page 2018

  5. [5]

    Bias in algorithmic filtering and personalization,

    Bozdag, E., “Bias in algorithmic filtering and personalization,”Ethics and information technology, Vol. 15, 2013, pp. 209–227

    work page 2013

  6. [6]

    Decoding AI Judgment: How LLMs Assess News Credibility and Bias,

    Loru, E., Nudo, J., Di Marco, N., Cinelli, M., and Quattrociocchi, W., “Decoding AI Judgment: How LLMs Assess News Credibility and Bias,”arXiv preprint arXiv:2502.04426, 2025

    arXiv 2025

  7. [7]

    Evaluation of Reliability Criteria for News Publishers with Large Language Models,

    Pratelli, M., Bianchi, J., Pinelli, F., and Petrocchi, M., “Evaluation of Reliability Criteria for News Publishers with Large Language Models,”Proceedings of the 17th ACM Web Science Conference 2025, 2025, pp. 179–188

    work page 2025

  8. [8]

    WeaklysupervisedveracityclassificationwithLLM-predicted credibility signals,

    Leite,J.A.,Razuvayevskaya,O.,Bontcheva,K.,andScarton,C.,“WeaklysupervisedveracityclassificationwithLLM-predicted credibility signals,”EPJ Data Science, Vol. 14, No. 1, 2025, p. 16

    work page 2025

Show all 23 references
  1. [9]

    LExT: Towards Evaluating Trustworthiness of Natural Language Explanations,

    Shailya, K., Rajpal, S., Krishnan, G. S., and Ravindran, B., “LExT: Towards Evaluating Trustworthiness of Natural Language Explanations,”Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency, 2025, pp. 1565–1587

    2025

  2. [10]

    Towards responsible media recommendation,

    Elahi, M., Jannach, D., Skjærven, L., Knudsen, E., Sjøvaag, H., Tolonen, K., Holmstad, Ø., Pipkin, I., Throndsen, E., Stenbom, A., et al., “Towards responsible media recommendation,”AI and Ethics, 2022, pp. 1–12

    2022

  3. [11]

    Beyond optimizing for clicks: Incorporating editorial values in news recommendation,

    Lu, F., Dumitrache, A., and Graus, D., “Beyond optimizing for clicks: Incorporating editorial values in news recommendation,” Proceedings of the 28th ACM conference on user modeling, adaptation and personalization, 2020, pp. 145–153

    2020

  4. [12]

    GPT-4o System Card,

    OpenAI 2024, “GPT-4o System Card,” arXiv preprint arXiv:2410.21276, 2024. Multimodal model capable of processing text, image, and audio

    2024 arXiv

  5. [13]

    The Llama 3 Herd of Models,

    Grattafiori, A., Dubey, A., et al., “The Llama 3 Herd of Models,”arXiv preprint arXiv:2407.21783, 2024. Includes LLaMA 3 70B and 405B models

    2024 arXiv

  6. [14]

    Mistral Large: our new flagship language model,

    Mistral AI, “Mistral Large: our new flagship language model,”https://mistral.ai/news/mistral-large/, 2023

    2023

  7. [15]

    CommandR+ModelCard,

    Cohere,“CommandR+ModelCard,” https://docs.cohere.com/docs/command-r-plus,2024. VersionreleasedAugust 2024

    2024

  8. [16]

    Qwen-72B,

    Qwen Team, “Qwen-72B,”https://huggingface.co/Qwen/Qwen-72B, 2024

    2024

  9. [17]

    WizardLM: Empowering large pre-trainedlanguagemodelstofollowcomplexinstructions,

    Xu, C., Sun, Q., Zheng, K., Geng, X., Zhao, P., Feng, J., Tao, C., Lin, Q., and Jiang, D., “WizardLM: Empowering large pre-trainedlanguagemodelstofollowcomplexinstructions,” TheTwelfthInternationalConferenceonLearningRepresentations , 2024

    2024

  10. [18]

    Intraclass correlations: uses in assessing rater reliability

    Shrout, P. E., and Fleiss, J. L., “Intraclass correlations: uses in assessing rater reliability.”Psychological bulletin, Vol. 86, No. 2, 1979, p. 420

    1979

  11. [19]

    A guideline of selecting and reporting intraclass correlation coefficients for reliability research,

    Koo, T. K., and Li, M. Y., “A guideline of selecting and reporting intraclass correlation coefficients for reliability research,” Journal of chiropractic medicine, Vol. 15, No. 2, 2016, pp. 155–163

    2016

  12. [20]

    Cumulated gain-based evaluation of IR techniques,

    Järvelin, K., and Kekäläinen, J., “Cumulated gain-based evaluation of IR techniques,”ACM Transactions on Information Systems (TOIS), Vol. 20, No. 4, 2002, pp. 422–446

    2002

  13. [21]

    On the opportunities and risks of foundation models,

    Bommasani,R.,Hudson,D.A.,Adeli,E.,Altman,R.,Arora,S.,vonArx,S.,Bernstein,M.S.,Bohg,J.,Bosselut,A.,Brunskill, E., et al., “On the opportunities and risks of foundation models,”arXiv preprint arXiv:2108.07258, 2021. 15

    2021 arXiv

  14. [22]

    Language (technology) is power: A critical survey of

    Blodgett, S. L., Barocas, S., Daumé III, H., and Wallach, H., “Language (technology) is power: A critical survey of" bias" in nlp,”arXiv preprint arXiv:2005.14050, 2020

    2005 arXiv

  15. [23]

    Large language models reflect the ideology of their creators,

    Buyl, M., Rogiers, A., Noels, S., Bied, G., Dominguez-Catena, I., Heiter, E., Johary, I., Mara, A.-C., Romero, R., Lijffijt, J., et al., “Large language models reflect the ideology of their creators,”arXiv preprint arXiv:2410.18417, 2024. Appendix: Detailed PSA Criteria In this...

    arXiv

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Reviewed August 6, 2026 · model on record in the stance chip above.