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Paper Citation Record · LEDGER

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition

As of 23 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2505.07166.

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

pith.paper-citation-record.v1
2505.07166 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:27:44.498284Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

14 of 14 outbound references displayed

  • verified exact0
  • verified fuzzy11
  • unresolved3
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d4b1a9a6-8510-4e03-8518-60333e58ba13 · outbound

This paper cites Pruning-based methods in deep neural networks: A review.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Pruning-based methods in deep neural networks: A review

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:27:44.651643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T22:27:44.461623Z digest=sha256:553ac55f2070da0a9d60bb0abf0a221879f96d400048fe723e1304d743eba35f

Observation 3647cfc4-415d-4d20-aa56-98db8975b439 · outbound

This paper cites Contriever: A fully unsupervised dense retriever.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Contriever: A fully unsupervised dense retriever

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:27:44.640295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T22:27:44.465412Z digest=sha256:fbdf55fb81ef7ff8c9ba7908e750cbdc3f6fe3a3d56f8416678145ea7302de31

Observation 7d87be54-4e1b-413f-8b91-8c24168afe31 · outbound

This paper cites Dense passage retrieval for open-domain question answering.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Dense passage retrieval for open-domain question answering

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:27:44.628104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T22:27:44.469036Z digest=sha256:b0bad6de6c9c4198b01dcc052bb61bb8a2b3122937fe80f4ffc14d3e76336fa9

Observation 97f27550-d18a-45fa-a296-f542990d89f8 · outbound

This paper cites Natural questions: A benchmark for question answering.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Natural questions: A benchmark for question answering

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:27:44.615202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T22:27:44.472738Z digest=sha256:8f9b8c9d5c9fb0132272fa80383bafb3db6ac8e1c2f12ee3f777325c44631bf8

Observation c2e96770-9b10-454f-b61c-95cf2ae43097 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Sentence-bert: Sentence embeddings using siamese bert-networks

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:27:44.577608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T22:27:44.483036Z digest=sha256:5c65083342d7af4b1ab9ac2d5705807c3b555e893963e9112194f99cc2065383

Observation f1dde236-418c-48f0-a08f-b115a982bfc5 · outbound

This paper cites Replama: A decoder-based dense retriever for open-domain question answering.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Replama: A decoder-based dense retriever for open-domain question answering

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:27:44.565492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T22:27:44.486765Z digest=sha256:1ea005ee43d1155e67805d56b2750897099690255f7f557574ba24e7e38eaa2d

Observation 59371d71-578b-40bc-afaa-f957761750c5 · outbound

This paper cites 2D Matryoshka Training for Information Retrieval.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition 2D Matryoshka Training for Information Retrieval

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-15T22:27:44.494350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:27:44.494350Z digest=sha256:c48b193e4f08dac48bf143c138c0b451afa9b034907a8503e1192c9b02c222da

Observation 6e5f0728-3215-4cc4-87d7-5e8de5b8e3d1 · outbound

This paper cites PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-15T22:27:44.498284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:27:44.498284Z digest=sha256:ab2d92883e4329c76a1068f4e9d1f5b9b21ce5a7a40f29540d5bdbe237df835e

Observation 02a75ee3-f188-4a6f-92ef-2a64ed476709 · outbound

This paper cites Lecture Notes on Neural Information Retrieval.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Lecture Notes on Neural Information Retrieval

Reference 2017

Resolution
unresolved
no resolver link, observed 2026-08-15T22:27:44.490401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:27:44.490401Z digest=sha256:e11d8a14e745b4192eb746c09e2a180a7215d2706e2d923782e16f9b5b8afb71

Observation 00da0993-dc05-4649-a176-023210b775d6 · outbound

This paper cites Promptreps: Enhancing dense retrieval with prompt-based representations.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Promptreps: Enhancing dense retrieval with prompt-based representations

Reference 2019

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:27:44.601548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T22:27:44.476034Z digest=sha256:fd21531de82eac38a32a4ef326c2eb518eacd54b7ea375286b291ecada2ba2e8

Observation ecbff32f-d97b-496f-b93d-b16fe64ca71f · outbound

This paper cites Knowledge neurons in pre-trained transformers.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Knowledge neurons in pre-trained transformers

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:27:44.684098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T22:27:44.449289Z digest=sha256:8b81c6abd8770a35bc01ab72f87ff47bca5dfebc66b818c6db154a865a403390

Observation 4a50d862-45da-4184-9dd8-66ac78b0fca9 · outbound

This paper cites Transformer feed-forward layers are key-value memories.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Transformer feed-forward layers are key-value memories

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:27:44.662509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T22:27:44.457425Z digest=sha256:bf67d7290a0abf43f206ebff8a000f523678f3a815391a33bb46f1530626dad9

Observation 06a1c6d7-edbe-48b8-8c20-0b3a7b867693 · outbound

This paper cites Simcse: Simple contrastive learning of sentence embeddings.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Simcse: Simple contrastive learning of sentence embeddings

Reference 2022

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:27:44.673294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T22:27:44.453562Z digest=sha256:ab59f313cc3f7165dd586f27868fbee88a86f8df77d08fb9e41cc2522b22b756

Observation c984636a-7384-48bb-b2e3-6e561ee9d263 · outbound

This paper cites Pre-training for ad-hoc retrieval: hyperlink is also you need.

Pre-training vs. Fine-tuning: A Reproducibility Study on Dense Retrieval Knowledge Acquisition Pre-training for ad-hoc retrieval: hyperlink is also you need

Reference 2024

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:27:44.589345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T22:27:44.479234Z digest=sha256:ab791d9c35aa707d14e93225b0a4f0518aa596936c02a59df3299aa6662c9a6f

Pith citing papers

No inbound Pith citation observations are available.