Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone

Cristian Santini; Emanuele Frontoni; Laura Melosi

REVIEW 4 major objections 4 minor 26 references

Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Fine-tuned GliNER decisively outperforms LLaMa3.1-8B on a new NER benchmark built from Leopardi's Zibaldone.

desk verdict A useful new Italian historical NER benchmark, but the label provenance question needs a straight answer before the headline comparison is fully trusted. read the letter →

arxiv 2505.20113 v1 pith:VWNT5Z4O submitted 2025-05-26 cs.CL cs.AI

Cristian Santini , Laura Melosi , Emanuele Frontoni This is my paper · ORCID

classification cs.CLcs.AI

keywords namedentityrecognitionhistoricalItalianZibaldoneLLMevaluationGliNERLLaMadigitalhumanitieslinking

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

This paper builds a new benchmark for named entity recognition (NER) on historical Italian by extracting 2,899 person, location, and literary-work references from the hyperlinked digital edition of Giacomo Leopardi's Zibaldone. It evaluates four systems on this data: LLaMa3.1-8B prompted generatively and extractively, a zero-shot GliNER model, and the same GliNER model fine-tuned on the Zibaldone training split. The central claim is that the fine-tuned 90-million-parameter model substantially outperforms the 8-billion-parameter instruction-tuned model, reaching 68.98% exact and 75.64% fuzzy micro-F1 versus at most 32.58% exact F1 for LLaMa3.1. The authors conclude that instruction-tuned large language models still face serious difficulties with historical humanistic texts, while fine-tuned NER models offer more robust performance for digital-editing workflows.

What carries the argument

The dataset generation pipeline is the load-bearing instrument: the digital edition encodes entity references as hyperlinks to standard authority records, and the authors scrape 260 notes (pp. 2700-3000) for evaluation and 688 shorter notes from two other page ranges for training, converting the links into span-level annotations with PER, LOC, and WORK types. The model comparison hinges on the GliNER architecture, a bidirectional transformer that learns shared embeddings for text spans and entity-type names, fine-tuned here for four epochs on the Zibaldone training data, and on the instruction-tuned LLaMa3.1-8B used with two Italian prompts (generative re-annotate-the-text and extractive list-of-entities). Evaluation uses exact and fuzzy span matching with class-level and micro/macro averages.

What would settle it

Have independent annotators re-label a random sample of the held-out Zibaldone notes from scratch on the same three entity classes, compare those gold labels to the deposited silver labels to measure agreement, and recompute all four systems' exact and fuzzy F1 against the new gold set; if the fine-tuned GliNER advantage over LLaMa3.1-8B shrinks to statistical parity, the paper's central claim is not supported.

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Extended reading notes

Core claim

On the new Zibaldone evaluation set, the fine-tuned GliNER model dominates all four tested configurations on every metric, with micro-averaged F1 of 68.98% (exact) and 75.64% (fuzzy), while the strongest LLaMa3.1-8B configuration, the extractive prompt, reaches only 32.58% exact F1. In per-class results, the fine-tuned model achieves 89.75% exact precision on persons (92% fuzzy) and 81.25% on locations, but drops to 44.50% exact precision on literary works; the work class is hardest for every model, with non-fine-tuned models falling below 20% exact F1. The authors interpret this as evidence that domain-specific fine-tuning, not scale alone, determines NER reliability on historical literary Italian.

Load-bearing premise

The benchmark assumes the hyperlink-based annotations scraped from the digital edition are accurate, complete, and reliable enough to serve as ground truth, even though the deposited dataset is explicitly labeled 'Silver annotations.'

Editorial extensions

If this is right

Digital edition projects working with historical Italian can get more reliable entity annotations from a small model fine-tuned on their own corpus than from a large foundation model used out of the box.
The released Zibaldone dataset gives the research community a reusable, publicly available benchmark for NER and later entity linking on nineteenth-century Italian scholarly prose.
Because the 'work' class is the weakest point for all systems, progress on recognizing bibliographic and literary references (including metonymic references like 'Il Forcellini') is the clearest lever for improving historical NER.
Even the best model leaves room for error, so the authors' recommended deployment pattern is human-in-the-loop annotation, with automatic predictions reviewed by experts.

Reading between the lines

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

Beyond the paper, the comparison is asymmetric: the LLM is used zero-shot while GliNER is fine-tuned, so the result shows the value of task-specific tuning more than an inherent ceiling for 8B models; fine-tuning LLaMa on the same training split could narrow or change the ranking.
Beyond the paper, the benchmark's entity boundaries come from the digital edition's hyperlinks, which reflect editorial choices; entities that editors chose not to link are invisible to training and evaluation, so the reported difficulty may be optimistic relative to fully gold-annotated corpora.
Beyond the paper, a cheap validation of the ground truth, such as reporting inter-annotator agreement on a re-annotated sample, would strengthen the benchmark's usefulness for the community.
Beyond the paper, the same scraping-based labeling recipe could be applied to other hyperlinked digital editions, producing multi-author historical benchmarks and tests of whether the fine-tuned model's advantage transfers across authors.

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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 real contribution here is the dataset, ZibaldonED, and the first NER benchmark for historical Italian. That fills a genuine gap, and the authors deserve credit for shipping it: the scraping pipeline is described, the data is on Zenodo, the code is on GitHub, and the per-class results give a clear picture of where the hard cases live. The main comparison, fine-tuned GliNER versus zero-shot LLaMa and zero-shot GliNER, is straightforward and the large gap is consistent with what other groups have found for French, German, Chinese, and Ottoman historical texts. So the empirical pattern is not surprising, but it is now documented for Italian.

The soft spot is label provenance. The paper calls the annotations expert-made and encoded as links, but the Zenodo deposit cited as [19] is titled 'Silver annotations.' Silver normally means the labels were not independently validated as gold. There is no inter-annotator agreement and no gold-recall check reported. Because the same hyperlink-derived pipeline produced both training and test labels, the fine-tuned model is essentially optimized to reproduce the linkers' decisions, omissions included. LLaMa, with no in-domain training, is then penalized for finding valid mentions that DigitalZibaldone simply did not link. That asymmetry could inflate the reported gap. I do not think the labels are certainly wrong, and the domain experts are real, but the authors need to state explicitly what validation, if any, was done, or relabel the resource as silver and temper the claims accordingly.

Secondary issues are minor to moderate: no error bars or significance tests, only two fixed prompts for LLaMa with no prompt exploration, and the training split was filtered to notes with at most 350 tokens and at least one annotation while the test split appears unfiltered, so the two distributions differ. These are fixable in a revision.

Who should read this: Italian digital humanists and anyone building or evaluating NER tools for scholarly editions. The dataset is the lasting value; the model comparison is useful but secondary.

Recommendation: this deserves peer review, not desk rejection. A serious referee should push on label validation and ask for uncertainty measures, but the benchmark itself is worth publishing.

Referee Report

4 major / 4 minor

Summary. The paper introduces a new NER benchmark for historical Italian based on Giacomo Leopardi's Zibaldone, with 2,899 references to persons, locations, and literary works extracted from the DigitalZibaldone digital edition. The authors evaluate four systems: LLaMa3.1-8B-Instruct with a generative and an extractive prompt, a zero-shot GliNER model, and a GliNER model fine-tuned on the new training split. Using exact and fuzzy matching, they report micro-averaged F1 of 68.98% exact and 75.64% fuzzy for fine-tuned GliNER, versus at most 32.58% exact F1 for LLaMa3.1, and conclude that fine-tuned domain-specific NER models are more robust than instruction-tuned LLMs on historical humanistic texts. The paper also discusses per-class results, with the 'work' class emerging as the most difficult, and it releases the dataset on Zenodo and notebooks on GitHub.

Significance. If the benchmark and evaluation are trustworthy, the paper fills a clear gap: there is no public NER benchmark for historical Italian, and the comparison between a 90M-parameter fine-tuned model and an 8B instruction-tuned LLM is practically relevant for digital humanities projects. The release of the dataset, code, and reproducible training setup is a concrete contribution. The main conclusions align with prior work on historical NER in other languages, which also finds that task-specific fine-tuned models outperform zero-shot LLMs. However, the significance is conditional on the quality of the evaluation labels: the Zenodo deposit is titled 'Silver annotations', and no independent gold-standard validation or inter-annotator agreement is reported. If the labels are incomplete, the ranking between the fine-tuned model and the zero-shot LLMs could be an artifact of the annotation pipeline rather than a true difference in NER ability.

major comments (4)

[§3.1, dataset and reference [19]] The evaluation ground truth is derived from hyperlinks present in DigitalZibaldone, and the cited Zenodo deposit [19] is explicitly titled 'Silver annotations for Entity Disambiguation from Digitalzibaldone'. Silver annotations are generally not validated as gold. Because the same link-derived annotation pipeline produced both the training and test labels, the fine-tuned GliNER is optimized to reproduce the linkers' decisions, including any omissions and boundary choices, while the zero-shot LLaMa model is penalized for finding entities that DigitalZibaldone did not link. The paper reports no inter-annotator agreement, no gold-recall check, and no manual verification of a sample. This is load-bearing for the central claim that fine-tuned NER models are more robust than instruction-tuned LLMs. I ask the authors to (a) clarify the provenance and validation status of the labels, (b) report a manual or independent completeness check on a sample of the evaluation set (e.g., count unlinked mentions that a human annotator would label), and (c) if the labels are indeed silver, either upgrade a test subset to gold or soften the benchmark claim accordingly.
[§4, Tables 4 and 5] The paper states that the fine-tuned GliNER 'significantly outperforms the others across all metrics', but no significance tests, confidence intervals, or error bars are reported. The evaluation appears to be a single run for each model; for LLMs with stochastic decoding, this is problematic. The authors should report the number of runs, the variance across runs, or bootstrap confidence intervals for the F1 scores, at least for the main micro-averaged comparison in Table 4.
[§3.1, training and evaluation splits] The training set was filtered to notes with total length <=350 tokens and containing at least one annotated reference (pp. 1000-2001 and 3001-4000), whereas the evaluation set consists of 260 notes from pp. 2700-3000 without mention of the same filtering criteria. This distribution shift could affect both the fine-tuned model and the zero-shot models; for example, the fine-tuned model was not trained on longer, sparsely annotated notes, and LLaMa's zero-shot behavior may also differ on them. The paper should state whether the same filtering was applied to the test set, and if not, report test-set statistics and consider an additional evaluation on a filtered subset to make the comparison fair.
[§3.2.1, LLaMa prompting setup] The LLaMa3.1 evaluation uses only two hand-written prompts (generative and extractive) with no prompt optimization, no validation set, and no exploration of alternative formulations. This is acknowledged in the paper as a limitation, but it is nevertheless important for the conclusion: the reported gap between LLaMa and fine-tuned GliNER may partly reflect suboptimal prompting rather than an inherent limitation of instruction-tuned LLMs for historical NER. The authors should either test additional prompt variants (e.g., few-shot or different instruction phrasings) or explicitly frame the conclusion as specific to the two prompts used.

minor comments (4)

[Throughout] There are several typos and stylistic inconsistencies: 'mantain' (Section 3.2.1), '5×10 −6' and '1×10 −5' with odd spacing, and the use of commas as decimal separators in Tables 4 and 5, which is acceptable in Italian but should be made consistent with the journal's style.
[§4, Table 5] The 'Avg.' column appears to be the unweighted arithmetic mean of the per-class scores. This should be explicitly defined as macro-averaging in the text; if a different aggregation is intended, the caption should say so.
[§3.1, Table 3] The test set contains only 61 LOC annotations, which makes the per-class LOC results in Table 5 very sensitive to a few instances. The authors should note this low count when interpreting the 'place' class results.
[§5.1] The discussion of 'Il Forcellini' as an indirect reference is instructive, but it would be strengthened by a quantitative analysis of how often such indirect references occur in the test set, since they may be a major source of the WORK-class errors.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark comparison is a standard supervised train/test split with labels derived externally from DigitalZibaldone's editorial hyperlinks, not from the evaluated models.

full rationale

The paper's central claim—that a fine-tuned GliNER model outperforms instruction-tuned LLaMa3.1 on historical Italian NER—rests on a conventional evaluation. The dataset (Section 3.1) is constructed by scraping DigitalZibaldone's hyperlinks, which were created by domain experts and point to Wikidata/VIAF; these annotations constitute the gold standard. The fine-tuned GliNER model is trained on pages 1000-2001 and 3001-4000, while evaluation uses pages 2700-3000, a disjoint range. No model output is used to generate the labels, and no parameter is fit to the test set. The zero-shot LLaMa and GliNER baselines are evaluated against the same external gold standard. The citation of the authors' own Zenodo dataset [19] is transparent and does not substitute for an independent derivation. The title 'Silver annotations' and the lack of inter-annotator agreement are legitimate concerns about label quality and benchmark validity, but they are not circularity: the model predictions do not feed back into the labels, and the ranking is not forced by any definition or fitted quantity. The paper's results are reproducible from the released dataset and code, so the comparison is self-contained. No load-bearing self-citation, no uniqueness theorem imported from the authors, and no ansatz smuggled in via citation are present.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

This is an empirical benchmark, so the main burdens are data quality and experimental protocol. Ground truth is adopted from DigitalZibaldone, whose Zenodo title says 'silver annotations' while the text calls it expert-annotated. Hyperparameters and note-selection thresholds are hand-chosen. No new theoretical entities or conserved quantities are introduced.

free parameters (2)

Fine-tuning hyperparameters = LR 5e-6 (NER), 1e-5 (backbone), 4 epochs, batch 4, weight decay 0.01
Hand-chosen values in Section 3.2.2; not optimized on the evaluation set, but they affect the fine-tuned model's F1 and could understate or overstate its advantage.
Training note filter threshold = max 350 tokens, at least one annotation
Section 3.1 filters training notes by length and annotation presence to shape the training distribution; this selection affects how well the fine-tuned model transfers to the unfiltered evaluation notes.

assumptions (3)

domain assumption DigitalZibaldone annotations are complete and correct.
Ground truth for all metrics; if annotation coverage or correctness is imperfect, every score in Tables 4 and 5 shifts. Section 3.1.
domain assumption Training and evaluation page ranges are disjoint.
Training uses pp.1000-2001 and 3001-4000; evaluation uses pp.2700-3000. If notes overlap or share duplicated text, measured generalization is inflated. Section 3.1.
domain assumption LLaMa output generation is deterministic enough for single-run comparison.
Section 3.2.1 reports no temperature, sampling seed, or repetition across runs; if sampling is stochastic, reported F1 values lack error bounds.

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Cite this review

Pith. "Pith review of Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone." pith.science (2026). https://pith.science/paper/VWNT5Z4O

@misc{pith2026250520113,
  author       = {Pith},
  title        = {Pith review of: Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VWNT5Z4O}},
  note         = {Machine review of arXiv:2505.20113}
}

read the original abstract

The increased digitization of world's textual heritage poses significant challenges for both computer science and literary studies. Overall, there is an urgent need of computational techniques able to adapt to the challenges of historical texts, such as orthographic and spelling variations, fragmentary structure and digitization errors. The rise of large language models (LLMs) has revolutionized natural language processing, suggesting promising applications for Named Entity Recognition (NER) on historical documents. In spite of this, no thorough evaluation has been proposed for Italian texts. This research tries to fill the gap by proposing a new challenging dataset for entity extraction based on a corpus of 19th century scholarly notes, i.e. Giacomo Leopardi's Zibaldone (1898), containing 2,899 references to people, locations and literary works. This dataset was used to carry out reproducible experiments with both domain-specific BERT-based models and state-of-the-art LLMs such as LLaMa3.1. Results show that instruction-tuned models encounter multiple difficulties handling historical humanistic texts, while fine-tuned NER models offer more robust performance even with challenging entity types such as bibliographic references.

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

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