Paper Citation Record · LEDGER

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy

As of 12 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2412.06575.

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

pith.paper-citation-record.v1
2412.06575 v2

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T19:35:30.059110Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

37 of 37 outbound references displayed

verified exact0
verified fuzzy0
unresolved37
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5ebe1a62-5320-4364-a20c-c73f8ffe6507 · outbound

This paper cites GPT-4 Technical Report.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:29.975620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f40cb5ef-2994-4efc-9206-4f32cb4df82d · outbound

This paper cites Qwen Technical Report.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Qwen Technical Report

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:29.978272Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e6763447-a2e3-471e-b589-4c432df30725 · outbound

This paper cites Fine-Tuned 'Small' LLMs (Still) Significantly Outperform Zero-Shot Generative AI Models in Text Classification.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Fine-Tuned 'Small' LLMs (Still) Significantly Outperform Zero-Shot Generative AI Models in Text Classification

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:29.980781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df1fae64-c4c4-4804-bde0-74d680520213 · outbound

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

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:29.983444Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d90dfd4-9039-4a67-af88-d1536378ccad · outbound

This paper cites MoDS: Model-oriented Data Selection for Instruction Tuning.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy MoDS: Model-oriented Data Selection for Instruction Tuning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:29.985703Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fccf596-c6e1-4f1a-9332-2b0a902e5569 · outbound

This paper cites GLM: General Language Model Pretraining with Autoregressive Blank Infilling.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy GLM: General Language Model Pretraining with Autoregressive Blank Infilling

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:29.988212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d4b1e02-7d2b-430a-90d8-16b5260f8d0b · outbound

This paper cites Language Models for Text Classification: Is In-Context Learning Enough?.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Language Models for Text Classification: Is In-Context Learning Enough?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:29.990886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a6f5905-85c5-49e6-b54b-4951e9aba38c · outbound

This paper cites Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:29.993292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb6f4b40-9209-4376-befc-4ef2000d4ed5 · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:35:30.387818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7e55022b-d080-435f-a733-fdc576f93797 · outbound

This paper cites Scaling Laws for Neural Language Models.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Scaling Laws for Neural Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:29.997849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 584c379a-f227-4c3f-8cc0-4c3555e9905a · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.000310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f02c651-7100-44db-8c89-2c9c8ab8fad6 · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:35:30.377924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 44775957-0cc1-4b0d-890f-8b0472022c08 · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:35:30.371674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f04bbb74-47c2-47ca-8d7a-5e20fce40b27 · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.006740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9444660b-a991-4898-9e05-3a5f64eb8124 · outbound

This paper cites Data Generation Using Large Language Models for Text Classification: An Empirical Case Study.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Data Generation Using Large Language Models for Text Classification: An Empirical Case Study

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.009208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4712931-9fd8-44f1-8518-fe0d759791f7 · outbound

This paper cites Textbooks Are All You Need II: phi-1.5 technical report.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Textbooks Are All You Need II: phi-1.5 technical report

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.012101Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c7e57a9-46f3-4db0-afb8-3a553eafd75b · outbound

This paper cites Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.014729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c03039ad-c2d4-4d3c-9cff-7f8c3146b45f · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:35:30.361330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fa17e53d-2451-4e7b-8da8-53ffd0b35d37 · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.019904Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9455d53e-cd5b-4b31-ada8-76873e08674e · outbound

This paper cites Large Language Models: A Survey.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Large Language Models: A Survey

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.022528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 947396d9-f8da-4356-ae32-593c3d671802 · outbound

This paper cites Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.025224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 18e8d365-6fda-4cb5-98d9-61d11cf11a2a · outbound

This paper cites Large Language Models Meet NLP: A Survey.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Large Language Models Meet NLP: A Survey

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.027453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c42728d-4de9-4bcc-907b-205a6cb2d0da · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.029485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df7a6c7d-ae17-4a65-acc3-69b40657a835 · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.031307Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f0b8d09-06b8-41ed-815a-37dbe00a890b · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:35:30.343799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c8f320d3-1259-4567-9106-d17b9e17536c · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Gemma: Open Models Based on Gemini Research and Technology

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.034952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e1b5165-bd80-4be2-97fe-c92956e87e2e · outbound

This paper cites Gemma 2: Improving Open Language Models at a Practical Size.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Gemma 2: Improving Open Language Models at a Practical Size

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.037068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 577a843a-2ce8-4eae-8bc9-f2a999501702 · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:35:30.337885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 663a0c3f-9620-42dc-bb3f-8371712bce6c · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy LLaMA: Open and Efficient Foundation Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.041122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db74130e-89ce-424c-a013-a42c38c3fcb6 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.043186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f025d0d8-4910-4921-b772-409b336f7442 · outbound

This paper cites Qwen2 Technical Report.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Qwen2 Technical Report

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.045158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 064cf462-0c71-4e8a-932d-148d977f8045 · outbound

This paper cites Evaluating Large Language Models at Evaluating Instruction Following.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Evaluating Large Language Models at Evaluating Instruction Following

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.047355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4f50a37-9818-4e51-96d2-a8152629bb3c · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.049768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79452b72-9957-4640-8d14-b220723154eb · outbound

This paper cites an unresolved cited work.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-11T19:35:30.330274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5335a824-d493-43b0-9de5-d00bb0911c3c · outbound

This paper cites Pushing The Limit of LLM Capacity for Text Classification.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy Pushing The Limit of LLM Capacity for Text Classification

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.053699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2512c15-c423-44a1-aeb8-0a9ff19375b8 · outbound

This paper cites online" 'onlinestring :=.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy online" 'onlinestring :=

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.056672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10631037-3b3e-4a97-8f15-754138f5864f · outbound

This paper cites write newline.

Data Quality Enhancement on the Basis of Diversity with Large Language Models for Text Classification: Uncovered, Difficult, and Noisy write newline

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T19:35:30.059110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.