Paper Citation Record · LEDGER

Language Models to Support Multi-Label Classification of Industrial Data

As of 19 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:2504.15922.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.15922 v2

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:19:06.137594Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 68555bf1-73fa-4150-91e4-c3eb3c0b7c18 · outbound

This paper cites Multi-label requirements classification with large taxonomies,.

Language Models to Support Multi-Label Classification of Industrial Data Multi-label requirements classification with large taxonomies,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:19:06.780903Z

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Observation 873d678a-900d-4f0f-a450-cf697e70e186 · outbound

This paper cites Zero-shot learning for require- ments classification: An exploratory study,.

Language Models to Support Multi-Label Classification of Industrial Data Zero-shot learning for require- ments classification: An exploratory study,

Reference 2

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Observation 220bb8f7-a3dc-4bfb-a28a-cb2956a67b9e · outbound

This paper cites Requirements Classification for Smart Allocation: A Case Study in the Railway Industry,.

Language Models to Support Multi-Label Classification of Industrial Data Requirements Classification for Smart Allocation: A Case Study in the Railway Industry,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:19:06.764849Z

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Observation 85c71922-da3d-4308-a2d1-cd38b3d50ffd · outbound

This paper cites Empirical evaluation of tools for hairy requirements engineering tasks,.

Language Models to Support Multi-Label Classification of Industrial Data Empirical evaluation of tools for hairy requirements engineering tasks,

Reference 4

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Observation e42a650b-51ff-4aa1-ae74-9157a5e7aca3 · outbound

This paper cites Panel: Context-dependent evaluation of tools for NL RE tasks: Recall vs. precision, and beyond,.

Language Models to Support Multi-Label Classification of Industrial Data Panel: Context-dependent evaluation of tools for NL RE tasks: Recall vs. precision, and beyond,

Reference 5

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Observation 9b8ccd3c-1df2-4aac-815e-2bbc9b8d37ab · outbound

This paper cites Language Models are Few-Shot Learners,.

Language Models to Support Multi-Label Classification of Industrial Data Language Models are Few-Shot Learners,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:19:06.718947Z

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Observation 78b1fd5c-8ba2-456e-9c45-f037c82b73d5 · outbound

This paper cites A Survey on Evaluation of Large Language Models,.

Language Models to Support Multi-Label Classification of Industrial Data A Survey on Evaluation of Large Language Models,

Reference 7

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Observation 174e824c-e734-4de8-912a-63089ba29c94 · outbound

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Language Models to Support Multi-Label Classification of Industrial Data Unresolved cited work

Reference 8

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Observation 6950669a-cd77-4de8-943a-2c8ff50ff173 · outbound

This paper cites Requirements Classification and Reuse: Crossing Domain Boundaries,.

Language Models to Support Multi-Label Classification of Industrial Data Requirements Classification and Reuse: Crossing Domain Boundaries,

Reference 9

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Observation 27a888c9-36ce-4e4a-b8e7-889539270016 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Language Models to Support Multi-Label Classification of Industrial Data BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 10

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Observation 2003b39a-e7cc-4ca8-9577-c504ff74ce5d · outbound

This paper cites Multiple comparisons using rank sums,.

Language Models to Support Multi-Label Classification of Industrial Data Multiple comparisons using rank sums,

Reference 11

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Source-reported events for the cited work

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Observation 97380351-794b-4a9d-82aa-54629622af93 · outbound

This paper cites Improving Protein Function Prediction using the Hierarchical Structure of the Gene Ontology,.

Language Models to Support Multi-Label Classification of Industrial Data Improving Protein Function Prediction using the Hierarchical Structure of the Gene Ontology,

Reference 12

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Observation 7a7225a0-7ecf-4d9c-b11a-63725b385f02 · outbound

This paper cites Natural language requirements processing: a 4d vision,.

Language Models to Support Multi-Label Classification of Industrial Data Natural language requirements processing: a 4d vision,

Reference 13

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Observation 0c06981b-0f3a-4f81-8922-3e455e53e969 · outbound

This paper cites Con- structing and Using Software Requirement Patterns,.

Language Models to Support Multi-Label Classification of Industrial Data Con- structing and Using Software Requirement Patterns,

Reference 14

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Observation 71858bff-8f4e-40b9-bc4d-169d8135ad59 · outbound

This paper cites Computing Semantic Relatedness Using Wikipedia- based Explicit Semantic Analysis,.

Language Models to Support Multi-Label Classification of Industrial Data Computing Semantic Relatedness Using Wikipedia- based Explicit Semantic Analysis,

Reference 15

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Observation a94437f7-09cd-4300-940e-3ddf9b030ce0 · outbound

This paper cites NoRBERT: Transfer Learning for Requirements Classification,.

Language Models to Support Multi-Label Classification of Industrial Data NoRBERT: Transfer Learning for Requirements Classification,

Reference 16

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Observation d6675c6a-a8c4-47ef-84f7-0ff332528859 · outbound

This paper cites Reporting Experiments in Software Engineering,.

Language Models to Support Multi-Label Classification of Industrial Data Reporting Experiments in Software Engineering,

Reference 17

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Observation 474a7577-c84d-45ec-ae71-a1a88d543d85 · outbound

This paper cites Mistral 7B.

Language Models to Support Multi-Label Classification of Industrial Data Mistral 7B

Reference 18

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Observation 9688d1d4-5fa1-4425-a1c7-1ab88d90bcdd · outbound

This paper cites Mixtral of Experts.

Language Models to Support Multi-Label Classification of Industrial Data Mixtral of Experts

Reference 19

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Language Models to Support Multi-Label Classification of Industrial Data Vector Semantics and Embeddings,

Reference 20

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Observation 738bd7dc-9b7e-4e60-9f90-00be0e9b5d26 · outbound

This paper cites One Model To Learn Them All.

Language Models to Support Multi-Label Classification of Industrial Data One Model To Learn Them All

Reference 21

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Observation 41a7c163-e1b8-4d51-b531-8de6fc037528 · outbound

This paper cites A BERT-based transfer learning approach to text classification on software requirements specifications,.

Language Models to Support Multi-Label Classification of Industrial Data A BERT-based transfer learning approach to text classification on software requirements specifications,

Reference 22

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Observation 219d111b-6c74-4190-94a9-ce9b91944698 · outbound

This paper cites Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning,.

Language Models to Support Multi-Label Classification of Industrial Data Automatically Classifying Functional and Non-functional Requirements Using Supervised Machine Learning,

Reference 23

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Observation e350f7a9-bef0-4c2c-b2aa-aac1fcc2c60f · outbound

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Language Models to Support Multi-Label Classification of Industrial Data RoBERTa: A robustly optimized BERT pretraining approach,

Reference 24

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Source-reported events for the cited work

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Observation abe94c41-c078-4f4c-b63b-6569bdbfd234 · outbound

This paper cites Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models.

Language Models to Support Multi-Label Classification of Industrial Data Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models

Reference 25

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Observation 9d00ece6-8773-4c50-9f47-e26121ac8ab2 · outbound

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Language Models to Support Multi-Label Classification of Industrial Data Ultra-large-scale systems: The software challenge of the future,

Reference 26

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Observation 8b213f95-d3a1-42ab-97b8-bff70344cab8 · outbound

This paper cites Automatic Requirement Categorization of Large Natural Lan- guage Specifications at Mercedes-Benz for Review Improvements,.

Language Models to Support Multi-Label Classification of Industrial Data Automatic Requirement Categorization of Large Natural Lan- guage Specifications at Mercedes-Benz for Review Improvements,

Reference 27

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Observation 655a9b62-00cf-4738-99f3-f46794d5d88f · outbound

This paper cites A model for types and levels of human interaction with automation,.

Language Models to Support Multi-Label Classification of Industrial Data A model for types and levels of human interaction with automation,

Reference 28

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Observation 83b7a3c0-8f7c-4165-a6c6-258a862d069a · outbound

This paper cites Improving Language Understanding by Generative Pre-Training,.

Language Models to Support Multi-Label Classification of Industrial Data Improving Language Understanding by Generative Pre-Training,

Reference 29

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Language Models to Support Multi-Label Classification of Industrial Data Sentence-bert: Sentence embeddings using siamese bert-networks,

Reference 30

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Observation fb8949d0-4422-4bee-a36a-cf1e94231779 · outbound

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Language Models to Support Multi-Label Classification of Industrial Data The Two Word Test: A Semantic Benchmark for Large Language Models

Reference 31

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Observation acf870fc-3ed7-4af0-a16b-d0e6cc71b134 · outbound

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Language Models to Support Multi-Label Classification of Industrial Data The role of natural language in requirements engineering,

Reference 32

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Observation ea114585-5fd3-4402-8e49-709254a39695 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Language Models to Support Multi-Label Classification of Industrial Data DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 33

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Language Models to Support Multi-Label Classification of Industrial Data Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches,

Reference 34

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Observation 1511ff28-7d35-4435-b4cc-e57b7dc87244 · outbound

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Language Models to Support Multi-Label Classification of Industrial Data MPNet: Masked and permuted pre-training for language understanding,

Reference 35

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Observation bf483c4c-a0a3-44c9-b621-35ebaecb0f94 · outbound

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Language Models to Support Multi-Label Classification of Industrial Data EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models

Reference 36

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Observation d7c8680d-8c71-4d7c-bb90-c70bacaaac1c · outbound

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Language Models to Support Multi-Label Classification of Industrial Data Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 37

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Language Models to Support Multi-Label Classification of Industrial Data TT-RecS: The taxonomic trace recommender system,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:19:06.387474Z

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Observation ba0929b7-c433-4a39-9c8b-6a9c19ed09d4 · outbound

This paper cites Attention is All you Need,.

Language Models to Support Multi-Label Classification of Industrial Data Attention is All you Need,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:19:06.372595Z

Source-reported events for the cited work

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Observation a096fc3f-a21e-4247-8c28-42efc8df9f15 · outbound

This paper cites What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?.

Language Models to Support Multi-Label Classification of Industrial Data What Language Model Architecture and Pretraining Objective Works Best for Zero-Shot Generalization?

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:19:06.358313Z

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Observation 6f425cb8-df72-4fd1-847c-1b97e8e18abe · outbound

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Language Models to Support Multi-Label Classification of Industrial Data MiniLM: Deep self-attention distillation for task-agnostic compression of pre- trained transformers,

Reference 41

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Observation 6084f489-1c18-45bf-b640-c21b99f0f881 · outbound

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Language Models to Support Multi-Label Classification of Industrial Data Wohlin, P

Reference 42

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Observation 53aa5b98-757a-4840-975c-1b0c300aa2a4 · outbound

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Language Models to Support Multi-Label Classification of Industrial Data Natural language processing for requirements engineering: A systematic mapping study,

Reference 43

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Observation 009fd397-26a4-4277-bf27-cd24d61dc9a1 · outbound

This paper cites IEEE, Jan.

Language Models to Support Multi-Label Classification of Industrial Data IEEE, Jan

Reference 1993

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:19:06.429249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

No inbound Pith citation observations are available.