Pith. sign in

REVIEW 5 major objections 5 minor 41 references

GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface

T0 review · 5 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read GLiNER2 claims that a single 205M-parameter encoder, prompted with a schema made of special tokens, performs zero-shot named entity recognition, text classification, and hierarchical structured extraction in one CPU-deployable forward…

desk verdict GLiNER2 has credible classification/NER numbers and a clean engineering story, but its flagship hierarchical extraction and multi-task composition are never evaluated, so accept it as a system note with an unvalidated core. read the letter →

arxiv 2507.18546 v1 pith:6XUQWGJS submitted 2025-07-24 cs.CL cs.AI

Urchade Zaratiana , Gil Pasternak , Oliver Boyd , George Hurn-Maloney , Ash Lewis This is my paper · ORCID
classification cs.CLcs.AI
keywords informationextractionzero-shotnamedentityrecognitiontextclassificationhierarchicalstructuredmulti-taskcompositiontransformerencoderCPUinferenceschema-driveninterface
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

GLiNER2 aims to show that a single compact transformer encoder can replace three kinds of specialized systems—named entity recognition, text classification, and hierarchical structured extraction—by prompting the encoder with a schema written in special tokens. The paper argues that this unified design keeps the CPU-friendly, privacy-preserving deployment of GLiNER while reaching accuracy close to large proprietary LLMs: zero-shot classification average 0.72 across seven benchmarks, the best among open-source baselines tested, and a CrossNER F1 of 0.590 versus GPT-4o's 0.599. If true, organizations needing on-premises or low-cost extraction, such as PII redaction or finance and healthcare processing, could run one 205M-parameter model instead of several task-specific systems or a GPU-backed LLM. The paper also claims that composing multiple tasks into one forward pass keeps CPU latency nearly flat as the number of classification labels grows, unlike label-by-label baselines.

What carries the argument

The load-bearing mechanism is the special-token prompt format with learned embeddings. A small set of special tokens—[P], [E], [C], [L], [SEP]—is randomly initialized and learned during training, and every input is assembled as $[\text{Task Prompt}] \oplus [\text{SEP}] \oplus \text{text}$. For NER, each [E] token yields an entity-type embedding and all candidate spans are scored by $\mathrm{score}(s_i, e_j) = \mathrm{sim}(h_{s_i}, h_{e_j})$ with a sigmoid activation. For classification, each [L] token produces a label embedding that an MLP projects to a logit, using softmax for single-label and sigmoid for multi-label decisions. For hierarchical extraction, an MLP reads the [P] token embedding to predict the number $K$ of parent instances (a 20-class head for counts 0–19), and learned occurrence-ID embeddings are added to each [C] attribute embedding to create $K$ distinct per-instance attribute representations, which are then matched to spans with the same scoring function as NER. This unified span-and-label matching mechanism is what allows one forward pass to handle multiple tasks at once.

What would settle it

Run the released gliner2-base model on documents containing more than 19 parent instances (for example, a product listing with 25 products) and on the seven classification benchmarks in both multi-task and single-task prompt form; if extraction counts saturate at 19 or per-task accuracy drops whenever other tasks share the forward pass, the hierarchical-composition claim fails.

Watch

Extended reading notes

Core claim

The central claim is that the GLiNER prompting paradigm—concatenating task-specification tokens with the input text through a [SEP] token—is expressive enough to cover NER, text classification, and hierarchical structured extraction in one model. GLiNER2 formulates every task as $[\text{Task Prompt}] \oplus [\text{SEP}] \oplus \text{text}$; [E] tokens carry entity-type embeddings matched to text spans by dot-product similarity, [L] tokens carry label embeddings passed through an MLP for softmax or sigmoid classification, and [P] tokens feed an MLP that predicts the number $K$ of parent instances, after which occurrence-ID embeddings condition [C] attribute tokens so each instance gets its own field representations. The paper reports that this one 205M-parameter system attains a 0.72 average zero-shot classification accuracy across seven benchmarks—highest among the open-source baselines tested (GLiClass 0.63, DeBERTa-v3 0.69)—and a 0.590 average F1 on CrossNER, within 0.009 of GPT-4o and 0.025 of the NER-dedicated GLiNER-M. The efficiency claim is that all labels are scored in a single forward pass, so CPU latency grows only mildly with label count (130–208 ms for 5–50 labels), whereas DeBERTa scales linearly (1.7–16.9 s).

Load-bearing premise

The load-bearing premise is that the process of counting how many parent records appear in the text and building separate attribute slots for each record keeps working on documents very different from the training data, and that bundling several tasks into one prompt does not make any single task worse; the paper never measures either of these.

Editorial extensions

If this is right

  • One 205M-parameter on-premises model can replace separate NER, classification, and structured-extraction models for many production deployments, without GPU requirements and under the Apache 2.0 license.
  • Zero-shot classification accuracy is competitive with much larger open models: the highest average among tested open baselines (0.72), within 12 points of GPT-4o (0.84).
  • CrossNER zero-shot NER F1 (0.590) lands within 0.009 of GPT-4o (0.599) and within 0.025 of a dedicated NER model, despite the multi-task training.
  • CPU inference latency stays roughly flat as label count grows (130–208 ms for 5–50 labels), about 2.6× faster than GPT-4o API calls and far faster than DeBERTa's label-by-label forward passes.
  • Composing NER, classification, and structured extraction in one schema produces all outputs in a single forward pass, so downstream pipelines need one model instead of a cascade of specialized systems.

Reading between the lines

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

  • The count predictor caps $K$ at 0–19, so documents containing more than 19 parent instances (for example, a listing with 25 products) would likely truncate or misassign instances; this boundary is never stress-tested and would be a simple failure probe.
  • The paper does not measure whether multi-task prompt concatenation degrades per-task accuracy; an ablation comparing single-task versus multi-task prompts on each benchmark would separate the cost of composition from the benefit of shared context.
  • The schema interface invites a natural benchmark: zero-shot hierarchical extraction currently has no established test set, so building one from documents with nested ground-truth structures would let the model's central new capability be compared directly against LLMs.
  • If the approach holds, it suggests a broader design rule for compact NLP: treat labels, entity types, and schema fields uniformly as prompted token embeddings, making task boundaries an interface choice rather than an architectural commitment.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper introduces GLiNER2, a compact encoder-based system that extends GLiNER to support zero-shot named entity recognition, text classification, hierarchical structured extraction, and composed multi-task inference in a single forward pass. The system uses a schema-driven prompt format with special tokens ([P], [E], [C], [L], [SEP]) and is released as a pip-installable library with a Gradio demo. The experimental section reports zero-shot results on seven text-classification benchmarks and the CrossNER NER benchmark, comparing against GPT-4o, GLiClass, DeBERTa-v3, and GLiNER-M. The authors report competitive classification performance (average 0.72 vs. 0.84 for GPT-4o) and near-matching NER F1 (0.590 vs. 0.599 for GPT-4o), along with CPU latency measurements. The paper explicitly states that hierarchical structure extraction was not evaluated due to the lack of established zero-shot benchmarks, and no experiments test the multi-task composition format.

Significance. If the central claims are borne out, GLiNER2 would be a practically valuable contribution: a 205M-parameter, CPU-deployable model that handles several IE tasks through one interface, reducing fragmentation across specialized systems. The paper's strengths are its use of standard external benchmarks for classification and NER, the absence of fit-to-test-set circularity, and the release of code, weights, and documentation. The differentiator over prior work, however, is hierarchical structured extraction and multi-task composition, and these capabilities are not evaluated at all. The current evidence supports the classification and NER results, but not the flagship claims in the abstract and introduction. The paper is best seen as a system demonstration; the missing evaluations are the main obstacle to accepting the stated contributions.

major comments (5)
  1. [§3.2, Appendix A] The headline differentiator of the paper—hierarchical structured extraction—is never evaluated. Section 3.2 states: 'Hierarchical structure extraction was not evaluated due to the absence of established zero-shot benchmarks for this task type,' yet the abstract and introduction claim support for 'hierarchical structured data extraction' and 'complex extraction schemas' as a core contribution. The architecture in Appendix A is therefore load-bearing with no supporting evidence. I request at least a diagnostic evaluation, even if on a self-constructed or newly released benchmark: report count-prediction accuracy (K vs. true count), attribute-level extraction F1, and instance-grouping coherence on documents with known structure. Without such evidence, the central claim that GLiNER2 unifies these tasks is unsubstantiated.
  2. [Appendix A] The count-prediction mechanism in Appendix A has a hard structural limit that is never stated as a limitation in the main text. The MLP performs 20-class classification for counts 0–19, so any document with 20 or more parent instances cannot be represented at all. Moreover, an error in the predicted K irreversibly corrupts all downstream field extraction because K determines the number of conditioned [C] embeddings. The authors must (i) explicitly state this 19-instance ceiling, (ii) measure count accuracy on held-out data, including the distribution of errors, and (iii) describe the behavior when the true count exceeds 19.
  3. [Appendix A, Task Composition] The claimed multi-task composition format, which concatenates task prompts with [SEP] and processes them in a single forward pass, is never compared against separate per-task forward passes. No experiment measures whether composed inputs degrade per-task accuracy due to cross-task interference, which is a known risk in multi-task prompting. The authors should add a direct comparison on the existing benchmarks: run the same text with (a) a single task prompt and (b) a composed prompt containing multiple tasks, and report per-task metrics. This is essential to support the 'efficient multi-task inference' and 'shared contextual understanding' claims in Sections 1 and 2.
  4. [§3.3, Table 4] The latency comparison mixes local CPU inference with a remote API call to GPT-4o. The '2.6× speedup over GPT-4o while running on standard CPU hardware' is a comparison of wall-clock API latency, which includes network and service overhead, not a CPU-vs-CPU or same-hardware measurement. As written, Table 4 conflates deployment accessibility with API round-trip time and overstates the efficiency gain. The authors should either remove the GPT-4o column from the CPU latency table, label it explicitly as end-to-end API latency with a clear caveat, or add a local LLM baseline measured on the same CPU hardware.
  5. [Appendix A, Hierarchical Structure Extraction] The span-scoring mechanism described in Appendix A does not by construction enforce instance coherence. For each instance-attribute pair, the model computes similarity scores with all text spans independently, and the description says the model selects the highest-scoring spans 'while maintaining instance coherence,' but no constraint or post-processing step is specified. Nothing prevents the same span from being selected for two different fields of the same instance, or for two different instances, nor does the mechanism guarantee that a name and price assigned to instance k come from the same underlying product mention. The authors should specify the decoding procedure (e.g., global span-pair constraints, non-maximum suppression, or a grouping objective) and report grouping accuracy in the diagnostic evaluation.
minor comments (5)
  1. [§3.2, Table 3] The sentence 'achieves higher scores in AI (0.526 vs. 0.547)' is factually wrong because Table 3 shows GLiNER2's AI F1 (0.526) is lower than GPT-4o's (0.547), not higher.
  2. [Abstract] The phrase 'Built pretrained transformer encoder architecture' appears to be missing a word; it should likely be 'Built on a pretrained transformer encoder architecture.'
  3. [Figures 1–8] Figures 2 through 8 repeat almost the entire code listing from Figure 1, and several captions do not match the content of the listing (e.g., Figure 2, captioned for NER, includes classification and schema code). This makes the figures hard to navigate and unnecessarily lengthens the paper. Figures should be trimmed to just the relevant API call rather than the full repeated listing.
  4. [§1] The introduction claims the model supports 'nested/overlapping spans,' but no experiment or architectural detail is given for this capability. Please clarify whether this is a supported feature and, if so, provide at least one example or reference to a test.
  5. [Table 4] The 'Speedup' row is ambiguous because the speedup relative to GPT-4o changes with label count (e.g., 2.75× at 5 labels vs. 2.62× overall). Please state the exact protocol used to compute the reported speedup value.

Circularity Check

0 steps flagged · score 2.0 of 10

No meaningful circularity; only minor self-citation to GLiNER as predecessor/baseline, which is not load-bearing.

full rationale

The paper's central experimental claims are checked against independent standard benchmarks (CrossNER, SST-2, IMDB, SNIPS, Banking77, Amazon-Intent, AG News, 20 Newsgroups), and the training data (GPT-4o-annotated real documents plus synthetic examples) is not drawn from those benchmarks, so there is no fit-to-test-set circularity. The hierarchical count-prediction mechanism in Appendix A is a supervised component explicitly trained on ground-truth instance counts, not a parameter fitted to the evaluation target, so reporting it as a capability is standard supervised modeling rather than a prediction that reduces to its own inputs by construction. The only self-citations are to GLiNER (Zaratiana et al., 2024) as the architectural predecessor and as the source of the GLiNER-M baseline numbers; GLiNER is an external released system with independent benchmark results, and citing it does not determine GLiNER2's outputs. The paper explicitly acknowledges that hierarchical structure extraction was not evaluated due to the absence of established zero-shot benchmarks, which is a completeness limitation rather than a circular derivation. Overall, the derivation chain from architecture to reported accuracy is self-contained against external benchmarks; no load-bearing step reduces to its own inputs.

Assumptions & free parameters 0 free parameters · 5 assumptions · 1 invented entities

The paper introduces model components (special tokens, occurrence-ID embeddings) and relies on several unstated assumptions about annotation quality, count coverage, and multi-task interference. No parameters were fitted to the evaluation benchmarks.

assumptions (5)
  • standard math Pretrained transformer encoder representations are suitable for span-based entity scoring (Eq. 1).
    The model builds on GLiNER, which established this; the paper reuses it without new justification.
  • domain assumption GPT-4o generated annotations on the collected documents are accurate enough to train a zero-shot model without manual validation.
    Section 3.1 says all documents were automatically annotated with GPT-4o and 'validated for quality' but no human agreement or annotation-quality numbers are reported.
  • ad hoc to paper The count predictor's 20-class output (counts 0-19) is sufficient for all realistic documents.
    Appendix A defines the MLP as 20-class classification for counts 0-19; documents with 20 or more instances of a parent entity cannot be represented.
  • ad hoc to paper Concatenating multiple task prompts with [SEP] avoids cross-task interference in a single forward pass.
    Task composition is described in Appendix A, but no experiments test whether composed extraction degrades accuracy per task.
  • ad hoc to paper Occurrence-ID embeddings learned during training generalize to unseen instance counts.
    The hierarchical extractor generates K conditioned representations using learned occurrence embeddings, with no evidence beyond the training distribution.
invented entities (1)
  • Special tokens [P], [E], [C], [L] and occurrence-ID embeddings independent evidence
    purpose: Mark task prompts, entity types, child fields, and classification labels, and disambiguate repeated parent instances in hierarchical extraction.
    These are newly introduced model components whose effectiveness is testable through the released model, though the paper provides no ablation isolating their contribution.

how reviews work

0 comments
Cite this review

Pith. "Pith review of GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface." pith.science (2026). https://pith.science/paper/6XUQWGJS

@misc{pith2026250718546,
  author       = {Pith},
  title        = {Pith review of: GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6XUQWGJS}},
  note         = {Machine review of arXiv:2507.18546}
}
read the original abstract

Information extraction (IE) is fundamental to numerous NLP applications, yet existing solutions often require specialized models for different tasks or rely on computationally expensive large language models. We present GLiNER2, a unified framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model. Built pretrained transformer encoder architecture, GLiNER2 maintains CPU efficiency and compact size while introducing multi-task composition through an intuitive schema-based interface. Our experiments demonstrate competitive performance across extraction and classification tasks with substantial improvements in deployment accessibility compared to LLM-based alternatives. We release GLiNER2 as an open-source pip-installable library with pre-trained models and documentation at https://github.com/fastino-ai/GLiNER2.

Figures

Figures reproduced from arXiv: 2507.18546 by the authors.

Figure 4
Figure 4. Composing multiple hierarchical structures in results = extractor.extract_entities(text, entity_descriptions) # Load from Hugging Face [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 6
Figure 6. Simultaneous multi-task classification. Task Composition A key feature of the library is its ability to efficiently compose multiple extraction tasks within a single unified framework [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. GLiNER2 Gradio demo interface showing hierarchical structure extraction. c. CEO Tim Cook announced the new iPhone 15 Pro no yesterday. The device features advanced [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figures from the paper (1 more)
Figure 8
Figure 8. Figure 8: Comprehensive task composition combining [PITH_FULL_IMAGE:figures/full_fig_p006_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

41 extracted references · 16 canonical work pages

  1. [1]

    Robin Armingaud. 2025. Glidre: Mod \`e le g \'e n \'e raliste pour l'extraction de relations \`a l' \'e chelle de documents. In EGC-Atelier TextMine

    work page 2025

  2. [2]

    Sergei Bogdanov, Alexandre Constantin, Timoth \'e e Bernard, Benoit Crabb \'e , and Etienne P Bernard. 2024. https://doi.org/10.18653/v1/2024.emnlp-main.660 N u NER : Entity recognition encoder pre-training via LLM -annotated data . In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 11829--11841, Miami, Florid...

    doi:10.18653/v1/2024.emnlp-main.660 2024

  3. [3]

    Jack Boylan, Chris Hokamp, and Demian Gholipour Ghalandari. 2025. https://doi.org/10.18653/v1/2025.naacl-long.418 GL i REL - generalist model for zero-shot relation extraction . In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers),...

    doi:10.18653/v1/2025.naacl-long.418 2025

  4. [4]

    I \ n igo Casanueva, Tadas Tem c inas, Daniela Gerz, Matthew Henderson, and Ivan Vuli \'c . 2020. https://doi.org/10.18653/v1/2020.nlp4convai-1.5 Efficient intent detection with dual sentence encoders . In Proceedings of the 2nd Workshop on Natural Language Processing for Conversational AI, pages 38--45, Online. Association for Computational Linguistics

    doi:10.18653/v1/2020.nlp4convai-1.5 2020

  5. [5]

    CNIL. 2024. https://www.cnil.fr/en/ai-and-gdpr-cnil-publishes-new-recommendations-support-responsible-innovation AI and GDPR: the CNIL publishes new recommendations to support responsible innovation

    work page 2024

  6. [6]

    Alessio Cocchieri, Giacomo Frisoni, Marcos Martínez Galindo, Gianluca Moro, Giuseppe Tagliavini, and Francesco Candoli. 2025. https://doi.org/10.18653/v1/2025.findings-naacl.47 OpenBioNER : Lightweight Open - Domain Biomedical Named Entity Recognition Through Entity Type Description . In Findings of the Association for Computational Linguistics : NAACL 20...

    work page doi:10.18653/v1/2025.findings-naacl.47 2025

  7. [7]

    Alice Coucke, Alaa Saade, Adrien Ball, Théodore Bluche, Alexandre Caulier, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Maël Primet, and Joseph Dureau. 2018. https://arxiv.org/abs/1805.10190 Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces . Prepri...

    arXiv 2018

  8. [8]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1423 BERT : Pre -training of Deep Bidirectional Transformers for Language Understanding . In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies , Volume 1 ( Lon...

    doi:10.18653/v1/n19-1423 2019

Show all 41 references
  1. [9]

    Hao Fei, Meishan Zhang, Min Zhang, and Tat-Seng Chua. 2024. https://doi.org/10.18653/v1/2024.acl-demos.3 XNLP : An Interactive Demonstration System for Universal Structured NLP . In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics ( Volum...

    2024 doi

  2. [10]

    Jack FitzGerald, Christopher Hench, Charith Peris, Scott Mackie, Kay Rottmann, Ana Sanchez, Aaron Nash, Liam Urbach, Vishesh Kakarala, Richa Singh, Swetha Ranganath, Laurie Crist, Misha Britan, Wouter Leeuwis, Gokhan Tur, and Prem Natarajan. 2022. https://arxiv.org/abs/2204.08...

    2022 arXiv

  3. [11]

    Ridong Han, Chaohao Yang, Tao Peng, Prayag Tiwari, Xiang Wan, Lu Liu, and Benyou Wang. 2024. https://arxiv.org/abs/2305.14450 An empirical study on information extraction using large language models . Preprint, arXiv:2305.14450

    2024 arXiv

  4. [12]

    Pengcheng He, Jianfeng Gao, and Weizhu Chen. 2023. https://openreview.net/forum?id=sE7-XhLxHA De BERT av3: Improving de BERT a using ELECTRA -style pre-training with gradient-disentangled embedding sharing . In The Eleventh International Conference on Learning Representations

    2023

  5. [13]

    Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd. 2020. https://doi.org/10.5281/zenodo.1212303 spaCy: Industrial-strength Natural Language Processing in Python

    2020 doi

  6. [14]

    Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas ...

    2023 arXiv

  7. [15]

    Knowledgator. 2025. gliclass-base-v1.0-lw. https://huggingface.co/knowledgator/gliclass-base-v1.0-lw

    2025

  8. [16]

    Ken Lang. 1995. https://api.semanticscholar.org/CorpusID:1921714 Newsweeder: Learning to filter netnews . In International Conference on Machine Learning

    1995

  9. [17]

    Xabier Lareo. 2023. https://www.edps.europa.eu/data-protection/technology-monitoring/techsonar/large-language-models-llm_en Large language models (llm) . https://www.edps.europa.eu/data-protection/technology-monitoring/techsonar/large-language-models-llm_en. European Data Prot...

    2023

  10. [18]

    Moritz Laurer, Wouter van Atteveldt, Andreu Casas, and Kasper Welbers. 2024. https://arxiv.org/abs/2312.17543 Building efficient universal classifiers with natural language inference . Preprint, arXiv:2312.17543

    2024 arXiv

  11. [19]

    Zihan Liu, Yan Xu, Tiezheng Yu, Wenliang Dai, Ziwei Ji, Samuel Cahyawijaya, Andrea Madotto, and Pascale Fung. 2020. https://arxiv.org/abs/2012.04373 Crossner: Evaluating cross-domain named entity recognition . Preprint, arXiv:2012.04373

    2020 arXiv

  12. [20]

    Maas, Raymond E

    Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011. https://aclanthology.org/P11-1015/ Learning Word Vectors for Sentiment Analysis . In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics : ...

    2011

  13. [21]

    Christopher Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven Bethard, and David McClosky. 2014. https://doi.org/10.3115/v1/P14-5010 The Stanford CoreNLP Natural Language Processing Toolkit . In Proceedings of 52nd Annual Meeting of the Association for Computational Li...

    2014 doi

  14. [22]

    NuMind . 2024. Nuextract: A framework for structured data extraction. https://numind.ai/blog/nuextract-a-foundation-model-for-structured-extraction

    2024

  15. [23]

    Mary Ellen Okurowski. 1993. https://doi.org/10.3115/1119149.1119164 Information extraction overview . In TIPSTER TEXT PROGRAM: PHASE I : Proceedings of a Workshop held at Fredricksburg, Virginia, September 19-23, 1993 , pages 117--121, Fredericksburg, Virginia, USA. Associatio...

    1993

  16. [24]

    OpenAI. 2024. https://arxiv.org/abs/2303.08774 Gpt-4 technical report . Preprint, arXiv:2303.08774

    2024 arXiv

  17. [25]

    Presidio. 2024. https://microsoft.github.io/presidio/samples/python/gliner/ Using gliner as an external pii model

    2024

  18. [26]

    Peng Qi, Yuhao Zhang, Yuhui Zhang, Jason Bolton, and Christopher D. Manning. 2020. https://doi.org/10.18653/v1/2020.acl-demos.14 Stanza: A Python Natural Language Processing Toolkit for Many Human Languages . In Proceedings of the 58th Annual Meeting of the Association for Com...

    2020 doi

  19. [27]

    Maarten Van Segbroeck. 2024. https://gretel.ai/blog/gliner-models-for-pii-detection Gliner models for pii detection through fine-tuning on gretel-generated synthetic documents

    2024

  20. [28]

    Yashothara Shanmugarasa, Ming Ding, Mahawaga Arachchige Pathum Chamikara, and Thierry Rakotoarivelo. 2025. https://api.semanticscholar.org/CorpusID:279402631 Sok: The privacy paradox of large language models: Advancements, privacy risks, and mitigation

    2025

  21. [29]

    Manning, Andrew Ng, and Christopher Potts

    Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. https://aclanthology.org/D13-1170/ Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank . In Proceedings of the 2013 Conference on Em...

    2013

  22. [30]

    Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, W...

    2023 arXiv

  23. [31]

    Shuhe Wang, Xiaofei Sun, Xiaoya Li, Rongbin Ouyang, Fei Wu, Tianwei Zhang, Jiwei Li, Guoyin Wang, and Chen Guo. 2025. https://doi.org/10.18653/v1/2025.findings-naacl.239 GPT - NER : Named entity recognition via large language models . In Findings of the Association for Computa...

    2025 doi

  24. [32]

    Ralph Weischedel, Sean Boisen, Daniel Bikel, Robert Bobrow, Michael Crystal, William Ferguson, Allan Wechsler, and The PLUM Research Group. 1996. https://doi.org/10.3115/1119018.1119050 Progress in information extraction . In TIPSTER TEXT PROGRAM PHASE II: Proceedings of a Wor...

    1996

  25. [33]

    Yuhao Wu, Evin Jaff, Ke Yang, Ning Zhang, and Umar Iqbal. 2025. https://arxiv.org/abs/2408.13247 An in-depth investigation of data collection in llm app ecosystems . Preprint, arXiv:2408.13247

    2025 arXiv

  26. [34]

    An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng, Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge, Haoran Wei, Huan Lin, Jialong Tang, and 41 others. 2025. https://arxiv.org/abs/2505.09388 Qw...

    2025 arXiv

  27. [35]

    Anthony Yazdani, Ihor Stepanov, and Douglas Teodoro. 2025. https://arxiv.org/abs/2504.00676 Gliner-biomed: A suite of efficient models for open biomedical named entity recognition . Preprint, arXiv:2504.00676

    2025 arXiv

  28. [36]

    Urchade Zaratiana, Nadi Tomeh, Pierre Holat, and Thierry Charnois. 2024. https://doi.org/10.18653/v1/2024.naacl-long.300 GL i NER : Generalist model for named entity recognition using bidirectional transformer . In Proceedings of the 2024 Conference of the North American Chapt...

    2024 doi

  29. [37]

    Dawen Zhang, Pamela Finckenberg-Broman, Thong Hoang, Shidong Pan, Zhenchang Xing, Mark Staples, and Xiwei Xu. 2024. https://arxiv.org/abs/2307.03941 Right to be forgotten in the era of large language models: Implications, challenges, and solutions . Preprint, arXiv:2307.03941

    2024 arXiv

  30. [38]

    Xiang Zhang, Junbo Zhao, and Yann LeCun. 2016. https://arxiv.org/abs/1509.01626 Character-level convolutional networks for text classification . Preprint, arXiv:1509.01626

    2016 arXiv

  31. [39]

    Wenxuan Zhou, Sheng Zhang, Yu Gu, Muhao Chen, and Hoifung Poon. 2024. https://openreview.net/forum?id=r65xfUb76p Universal NER : Targeted distillation from large language models for open named entity recognition . In The Twelfth International Conference on Learning Representations

    2024

  32. [40]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  33. [41]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

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