Paper Citation Record · LEDGER

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts

As of 10 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2502.07131.

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

pith.paper-citation-record.v1
2502.07131 v3

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:46:41.735855Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

15 of 15 outbound references displayed

verified exact0
verified fuzzy0
unresolved12
parse uncertain0
malformed identifier1
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 93478c44-6f8a-488e-a695-e76ce637ccad · outbound

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

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-08T13:46:41.682364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98b86634-f108-445c-8c46-8e710563bf1c · outbound

This paper cites Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives

Reference 7

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T13:46:41.873629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3ae028dd-6b5f-4e22-b00c-c9f49811baca · outbound

This paper cites URL http://dx.doi.org/10.1093/bioinformatics/ btz682.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts URL http://dx.doi.org/10.1093/bioinformatics/ btz682

Reference 10

Resolution: malformed identifier; no resolver link, observed 2026-08-08T13:46:41.691432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02fa93c9-540b-4202-8d55-be08de932bcb · outbound

This paper cites Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T13:46:41.713795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 694528d9-e583-4674-9385-2271d2b3121c · outbound

This paper cites Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance

Reference 12

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T13:46:41.824600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b7d7b3c0-d56c-461e-bec8-09791e9b380f · outbound

This paper cites One Embedder, Any Task: Instruction-Finetuned Text Embeddings.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts One Embedder, Any Task: Instruction-Finetuned Text Embeddings

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T13:46:41.722624Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 534869e2-2af2-4374-a7bb-aed3c366b551 · outbound

This paper cites Do We Need Domain-Specific Embedding Models? An Empirical Investigation.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts Do We Need Domain-Specific Embedding Models? An Empirical Investigation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T13:46:41.727006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4dd84ba1-c576-4af3-9e90-373566e8cf57 · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts BloombergGPT: A Large Language Model for Finance

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T13:46:41.735855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 19933d97-2c1e-4a74-b47f-5e8c69c28003 · outbound

This paper cites Efficient Estimation of Word Representations in Vector Space.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts Efficient Estimation of Word Representations in Vector Space

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-08T13:46:41.704943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8de1f620-45eb-4cdf-b322-71bf1e063887 · outbound

This paper cites FinBERT: Financial Sentiment Analysis with Pre-trained Language Models.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts FinBERT: Financial Sentiment Analysis with Pre-trained Language Models

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-08T13:46:41.671620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09eed182-83d4-436b-b2db-011ddcbc32d8 · outbound

This paper cites MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-08T13:46:41.731506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d559a4f-020d-4868-b34f-c0a709499b5e · outbound

This paper cites an unresolved cited work.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts Unresolved cited work

Reference 2021

Resolution: unresolved; raw_fallback, observed 2026-08-08T13:46:41.928277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 831c44b6-2079-48aa-8b57-b210d7fa0f7a · outbound

This paper cites SimCSE: Simple Contrastive Learning of Sentence Embeddings.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts SimCSE: Simple Contrastive Learning of Sentence Embeddings

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-08T13:46:41.687010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 420c08e2-bdaf-4e02-b150-ccae86bfdf6f · outbound

This paper cites MTEB: Massive Text Embedding Benchmark.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts MTEB: Massive Text Embedding Benchmark

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T13:46:41.709678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88c30572-01a8-425b-ac37-a159e975845d · outbound

This paper cites Making Text Embedders Few-Shot Learners.

TWICE: What Advantages Can Low-Resource Domain-Specific Embedding Model Bring? -- A Case Study on Korea Financial Texts Making Text Embedders Few-Shot Learners

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-08T13:46:41.695812Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.