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Paper Citation Record · LEDGER

Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2403.01432.

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

pith.paper-citation-record.v1
2403.01432 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T04:17:09.279667Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T08:22:11.201213Z

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 bfee7631-30d6-44bd-af46-accee6c6df5a · inbound

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression cites this paper.

Can Compressed LLMs Truly Act? An Empirical Evaluation of Agentic Capabilities in LLM Compression Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-07T14:17:58.286417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:17:58.286417Z digest=sha256:669ae7463ba104bc093b10a459b839a15c748e6cf04c5cd2228bb9511f29c377

Observation 234e9fd1-c2c6-4ea6-b3ec-b6d445c6e9b5 · inbound

PDF Retrieval Augmented Question Answering cites this paper.

PDF Retrieval Augmented Question Answering Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-19T08:22:11.205057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-19T08:17:19.048914Z digest=sha256:09d951cd463cf8aabf7eeee40921bf465cb36f5a27ebc9c80fdff71405ae0d5e

Observation 1d5eed76-425d-4215-8288-a6bccf5134f9 · inbound

ChipLingo: A Systematic Training Framework for Large Language Models in EDA cites this paper.

ChipLingo: A Systematic Training Framework for Large Language Models in EDA Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge

Reference 11

Resolution
verified exact
arxiv_id, observed 2026-05-12T09:41:27.241837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-07T09:40:25.666277Z digest=sha256:02a126e00d9ed032b70a1c97af6f8fef393960b1f08f53370714fba6a2035a98

Observation de363068-f9b4-44c4-b543-e4e9f3151940 · inbound

Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications cites this paper.

Assessment of RAG and Fine-Tuning for Industrial Question-Answering-Applications Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-12T05:41:23.702421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=arxiv_source observed=2026-05-12T05:06:43.040359Z digest=sha256:634df8a7af01f4e4cbf3663ba2c1d67d789e270dc2bdaa0df80393b8997904f9

Observation c9b0400b-9c9a-4b1f-b0d4-dbcc120f0f28 · inbound

Self-evolving Agentic Customer Support System at LinkedIn cites this paper.

Self-evolving Agentic Customer Support System at LinkedIn Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-14T04:17:09.279667Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T04:17:09.279667Z digest=sha256:e123b80482584e7828a8544584e3bd313ad26cd19ac3bda12fec11ae28c2ba71