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Paper Citation Record · LEDGER

Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2403.10882.

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

pith.paper-citation-record.v1
2403.10882 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:03:43.370937Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T22:22:09.561718Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation be99a21a-9212-47b3-833c-06a1451604a7 · inbound

Detecting Voice Phishing with Precision: Fine-Tuning Small Language Models cites this paper.

Detecting Voice Phishing with Precision: Fine-Tuning Small Language Models Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T06:03:43.370937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:03:43.370937Z digest=sha256:9a5718730efe6d8147cd65da27d3279892e7be2ce48d286fe1cb02892e4910ae

Observation 5024c7dd-8fd4-4c00-b9c2-f09dcfae1bd0 · inbound

Thunder-LLM: Efficiently Adapting LLMs to Korean with Minimal Resources cites this paper.

Thunder-LLM: Efficiently Adapting LLMs to Korean with Minimal Resources Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-06T23:58:09.651040Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T23:58:09.651040Z digest=sha256:e4a5899f0b8410d386081b3c84b99cbb9a0fe4a74dd76fca988ba4f292737173

Observation ea2b0f3b-0e4e-4ab2-8dbd-9e8400190b3e · inbound

A Dual-Layered Evaluation of Geopolitical and Cultural Bias in LLMs cites this paper.

A Dual-Layered Evaluation of Geopolitical and Cultural Bias in LLMs Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean

Reference 9

Resolution
verified exact
local_arxiv, observed 2026-08-06T22:22:09.620044Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-08-06T22:22:07.034181Z digest=sha256:2417aab287f5ab00005def66ec2755df1f333e80421921dbb66050f06ea34e3a