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Paper Citation Record · LEDGER

Fine-tuning Large Language Models for Entity Matching

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.08185.

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

pith.paper-citation-record.v1
2409.08185 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:21:32.887536Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T17:21:33.082512Z

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 e2150c1d-0063-4e8c-ab7d-b1e0e034271a · inbound

Beyond Traditional Algorithms: Leveraging LLMs for Accurate Cross-Border Entity Identification cites this paper.

Beyond Traditional Algorithms: Leveraging LLMs for Accurate Cross-Border Entity Identification Fine-tuning Large Language Models for Entity Matching

Reference 42

Resolution
verified exact
local_arxiv, observed 2026-08-06T17:21:33.087960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T17:21:32.887536Z digest=sha256:e85495ecea2e019244bebc520d1bae09a50a6ab7d0300d28be3681a1490fc545

Observation 54c8765e-fbd8-4643-b445-58b12333e80f · inbound

Entity Resolution in Practice: Lessons from a Self-Serve Pipeline cites this paper.

Entity Resolution in Practice: Lessons from a Self-Serve Pipeline Fine-tuning Large Language Models for Entity Matching

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-01T00:18:27.891645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T00:18:27.891645Z digest=sha256:f194ab344fdbbcde14918a8766b3f86b5f8bd2950044adbdb5204b89f7204c45