Paper Citation Record · LEDGER

Small Language Models in the Real World: Insights from Industrial Text Classification

As of 20 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.16078.

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

pith.paper-citation-record.v1
2505.16078 v3

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:09:56.505004Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

41 of 41 outbound references displayed

verified exact6
verified fuzzy0
unresolved35
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ebb90dad-de30-4a3b-9605-5b6bc0b81231 · outbound

This paper cites Longformer: The Long-Document Transformer.

Small Language Models in the Real World: Insights from Industrial Text Classification Longformer: The Long-Document Transformer

Reference 1

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Source-reported events for the cited work

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Observation 57be1821-85b8-409c-8c51-7326c5c173d4 · outbound

This paper cites Language Models are Few-Shot Learners.

Small Language Models in the Real World: Insights from Industrial Text Classification Language Models are Few-Shot Learners

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:52.977920Z

Source-reported events for the cited work

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Observation be81c185-fa08-4161-872c-8ccf462636da · outbound

This paper cites an unresolved cited work.

Small Language Models in the Real World: Insights from Industrial Text Classification Unresolved cited work

Reference 4

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Source-reported events for the cited work

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Observation 1113b452-759a-4b74-89b9-93cb02e9fb1b · outbound

This paper cites an unresolved cited work.

Small Language Models in the Real World: Insights from Industrial Text Classification Unresolved cited work

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:53.134654Z

Source-reported events for the cited work

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Observation da56d67b-407a-4b2f-8cfa-def08a2c4331 · outbound

This paper cites Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks.

Small Language Models in the Real World: Insights from Industrial Text Classification Evolutionary Data Measures: Understanding the Difficulty of Text Classification Tasks

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:09:58.572634Z

Source-reported events for the cited work

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Observation e8abeaa0-b3a0-4373-9aea-9dd2f8bbe51e · outbound

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

Small Language Models in the Real World: Insights from Industrial Text Classification BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:53.325075Z

Source-reported events for the cited work

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Observation e3cb891a-5df6-4657-83c1-abc1f007fba7 · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification Unresolved cited work

Reference 9

Resolution: verified exact; doi, observed 2026-08-07T15:09:56.975482Z

Source-reported events for the cited work

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Observation 5518176e-9fd7-4a39-ace7-d16368d39387 · outbound

This paper cites Is Encoder-Decoder Redundant for Neural Machine Translation?.

Small Language Models in the Real World: Insights from Industrial Text Classification Is Encoder-Decoder Redundant for Neural Machine Translation?

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:09:58.416149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 40a9931a-de85-4e4d-b9a1-3c757ba5a030 · outbound

This paper cites an unresolved cited work.

Small Language Models in the Real World: Insights from Industrial Text Classification Unresolved cited work

Reference 11

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Source-reported events for the cited work

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Observation feb0859f-6396-46da-ad35-4f61479df4bf · outbound

This paper cites How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation.

Small Language Models in the Real World: Insights from Industrial Text Classification How Good Are GPT Models at Machine Translation? A Comprehensive Evaluation

Reference 12

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Source-reported events for the cited work

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Observation c0cf11bd-318d-409a-a12f-d0a78fc218d0 · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification Unresolved cited work

Reference 13

Resolution: verified exact; doi, observed 2026-08-07T15:09:56.857972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0ea797cd-b400-4ab2-90c8-e21dbd08bbba · outbound

This paper cites Convolutional Neural Networks for Sentence Classification.

Small Language Models in the Real World: Insights from Industrial Text Classification Convolutional Neural Networks for Sentence Classification

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:53.854168Z

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Observation 76682cc1-f6b2-4c58-809d-b957495c25c0 · outbound

This paper cites an unresolved cited work.

Small Language Models in the Real World: Insights from Industrial Text Classification Unresolved cited work

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:53.953813Z

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Observation 577b2b46-c21c-443c-b24e-924655ec6217 · outbound

This paper cites an unresolved cited work.

Small Language Models in the Real World: Insights from Industrial Text Classification Unresolved cited work

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d26dd0ec-29fe-4856-92bf-a8dd8a188fa1 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

Small Language Models in the Real World: Insights from Industrial Text Classification The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:54.210845Z

Source-reported events for the cited work

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Observation a547b08b-692f-4049-9b72-c6aa7202ba44 · outbound

This paper cites BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension.

Small Language Models in the Real World: Insights from Industrial Text Classification BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:54.313169Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8eb9c18-15ea-4325-a52f-e09e404e3acf · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

Small Language Models in the Real World: Insights from Industrial Text Classification Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:54.446604Z

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Observation 23e81b8f-ee6b-4c3c-9ef2-cdeebb9b9a6b · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification DeepSeek-V3 Technical Report

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:54.579104Z

Source-reported events for the cited work

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Observation 8c712384-404a-4ca8-ae67-201807b3f217 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Small Language Models in the Real World: Insights from Industrial Text Classification RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 21

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Observation 92c0df19-bd66-49aa-8310-ba2804f7b873 · outbound

This paper cites NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging.

Small Language Models in the Real World: Insights from Industrial Text Classification NER-BERT: A Pre-trained Model for Low-Resource Entity Tagging

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:54.766681Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79533b9f-55a5-4cfa-8fb7-1193df466ea2 · outbound

This paper cites A Comprehensive Overview of Large Language Models.

Small Language Models in the Real World: Insights from Industrial Text Classification A Comprehensive Overview of Large Language Models

Reference 23

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aff8009c-3d58-4a0b-bef3-94c638e2cde0 · outbound

This paper cites Which Student is Best? A Comprehensive Knowledge Distillation Exam for Task-Specific BERT Models.

Small Language Models in the Real World: Insights from Industrial Text Classification Which Student is Best? A Comprehensive Knowledge Distillation Exam for Task-Specific BERT Models

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:09:57.721189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d34ece1a-8a34-4a56-9baa-0b71ec5b3013 · outbound

This paper cites The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities.

Small Language Models in the Real World: Insights from Industrial Text Classification The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:55.095306Z

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Unavailable: canonical work link unavailable.

Observation c1d4af74-e3d6-4c6e-a9f5-0d1191e2deb9 · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification Unresolved cited work

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 02c548c2-5900-4555-8375-2eee076f0328 · outbound

This paper cites Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

Small Language Models in the Real World: Insights from Industrial Text Classification Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Reference 27

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Observation cf542a7d-5772-4f05-8b1c-029aac770d0a · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

Small Language Models in the Real World: Insights from Industrial Text Classification A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 28

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Observation 79fd70c6-ff55-4c16-a434-f964abc70e7c · outbound

This paper cites Yoo, Chan Yeun, Dirar Homouz, and Aya Taha.

Small Language Models in the Real World: Insights from Industrial Text Classification Yoo, Chan Yeun, Dirar Homouz, and Aya Taha

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:55.511039Z

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Observation 2982c0c8-dff5-4f8c-b7d0-f5721580dcf4 · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification Gemma 2: Improving Open Language Models at a Practical Size

Reference 30

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Observation cfe9a2c3-a940-47de-8976-c14cf210cbfe · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification LLaMA: Open and Efficient Foundation Language Models

Reference 31

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Unavailable: canonical work link unavailable.

Observation de88bda2-a579-4dc4-a024-0789e6c3600a · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Small Language Models in the Real World: Insights from Industrial Text Classification Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:55.728373Z

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Observation 4003bb40-3caf-4c9d-9ac0-1aacd69075b0 · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification Unresolved cited work

Reference 33

Resolution: verified exact; doi, observed 2026-08-07T15:09:56.726220Z

Source-reported events for the cited work

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Observation 8a96b449-39b1-4486-b337-e99d300f7ec5 · outbound

This paper cites Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference.

Small Language Models in the Real World: Insights from Industrial Text Classification Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:55.862627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6123519c-c038-487d-9162-c5d6933d3b23 · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:55.950171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba66ad8b-3e86-41e2-9819-2407bbab9182 · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification Chain of Draft: Thinking Faster by Writing Less

Reference 36

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Observation ca4836d0-f800-4b1a-b025-9a13bc96e4e0 · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification Prompt Engineering a Prompt Engineer

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:56.120237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2809f73-bd89-4bb1-9ac6-36b76fde5f26 · outbound

This paper cites Generative and Discriminative Text Classification with Recurrent Neural Networks.

Small Language Models in the Real World: Insights from Industrial Text Classification Generative and Discriminative Text Classification with Recurrent Neural Networks

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:56.200252Z

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Observation 636c8604-34f3-48e7-89fb-f2fa259b6653 · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:09:58.772866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 350949e1-89b0-4d9e-9b3f-fcab29ef3baf · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification How do Large Language Models Handle Multilingualism?

Reference 40

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Observation b11178c9-0731-4573-a590-e6b0e2d6201b · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification Unresolved cited work

Reference 41

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Observation 61ce1bd4-dbb2-484e-b712-03e1cc341f7e · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification online" 'onlinestring :=

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Observation c9246cad-1934-4f39-8348-fb9980eb4362 · outbound

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Small Language Models in the Real World: Insights from Industrial Text Classification write newline

Reference 43

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Pith citing papers

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