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Paper Citation Record · LEDGER

Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2311.05800.

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

pith.paper-citation-record.v1
2311.05800 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T19:07:21.810937Z

measured 0 of 1 external citation measurements

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

Source: cited_works

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 64cdf801-3c1e-4c2f-a2d2-440faf1e92f2 · inbound

Boosting Data Utilization for Multilingual Dense Retrieval cites this paper.

Boosting Data Utilization for Multilingual Dense Retrieval Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval

Reference 59

Resolution
unresolved
no resolver link, observed 2026-08-04T19:07:21.810937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T19:07:21.810937Z digest=sha256:35c69e802f078a65a23a1677860c3264122c7cd523713c008f3fb6b621c60377

Observation 960f050e-dc8a-4b88-94c6-869b07b70424 · inbound

Bekko Embedding: Parameter-Efficient Multilingual Retrieval with Ultra-Compact Encoders cites this paper.

Bekko Embedding: Parameter-Efficient Multilingual Retrieval with Ultra-Compact Encoders Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval

Reference 100

Resolution
unresolved
no resolver link, observed 2026-08-01T03:16:00.709787Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T03:16:00.709787Z digest=sha256:3dbf15f03d6425cbb5fc6e23558753e5e819602fcdbc8232e3374a4b00c52036