Paper Citation Record · LEDGER

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks

As of 19 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 0 inbound Pith citation observations for arXiv:2504.15160.

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

pith.paper-citation-record.v1
2504.15160 v1

Coverage vector

measured 11 of 11 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:36:02.237119Z

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

11 of 11 outbound references displayed

verified exact3
verified fuzzy0
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a74f3719-3ba5-463d-8260-cfe98fe5e950 · outbound

This paper cites SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness.

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T11:36:02.200365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a9386b2-319c-4cd0-8e33-f1fd0da42fc8 · outbound

This paper cites Named Entity Recognition for Social Media Texts with Semantic Augmentation.

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks Named Entity Recognition for Social Media Texts with Semantic Augmentation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T11:36:02.205564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 510b898b-9d56-4368-bbe8-0699cfe1ba6b · outbound

This paper cites Text Augmentation in a Multi-Task View.

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks Text Augmentation in a Multi-Task View

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:36:02.330796Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation affdc4f9-7442-4e05-952f-1561aa92a292 · outbound

This paper cites EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks.

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-16T11:36:02.226392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4617a592-78f5-4995-81e1-ddda50ed3328 · outbound

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

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-16T11:36:02.195230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 696c9fd8-af7c-42a3-bc2c-0a960a60bcf1 · outbound

This paper cites Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation.

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-16T11:36:02.190319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd0b98ab-fe59-4114-be91-49177795c6e7 · outbound

This paper cites A Survey of Data Augmentation Approaches for NLP.

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks A Survey of Data Augmentation Approaches for NLP

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-16T11:36:02.184559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 520853bf-ee1c-4b8c-b4b2-d63667a5d0ec · outbound

This paper cites Should You Mask 15% in Masked Language Modeling?.

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks Should You Mask 15% in Masked Language Modeling?

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-16T11:36:02.231693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b7984de-0507-464f-ad97-7b1b83756b2b · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-16T11:36:02.237119Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c4dce33b-619d-4271-80f8-8db51dd24988 · outbound

This paper cites Identifying the sources of ideological bias in GPT models through linguistic variation in output.

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks Identifying the sources of ideological bias in GPT models through linguistic variation in output

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:36:02.354085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1fc114b5-d751-49aa-a77f-5176c5055895 · outbound

This paper cites Memory Is All You Need: Testing How Model Memory Affects LLM Performance in Annotation Tasks.

The Synthetic Imputation Approach: Generating Optimal Synthetic Texts For Underrepresented Categories In Supervised Classification Tasks Memory Is All You Need: Testing How Model Memory Affects LLM Performance in Annotation Tasks

Reference 2025

Resolution: verified exact; local_arxiv, observed 2026-08-16T11:36:02.376999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.