Pith. sign in

Paper Citation Record · LEDGER

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

As of 17 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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T22:27:44.461623Z digest=sha256:eb97d74d2b597eb1c50fbc88dd5a3a82bbdbd0a57834eb499d83cc88e8ef12fc

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T22:27:44.465412Z digest=sha256:51b1ed8b61ea96cbd509cdb40faaa3177b628328a851178f38f68692ade887dc

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T22:27:44.469036Z digest=sha256:df82a3cbd9b5811020b2a17d1dc796fd133a34f9dc19094486406d784b868478

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T22:27:44.472738Z digest=sha256:2d2798ea4e1b8b09209c2682ff1236fbbb06f8c9cfdec398dea4da3e051c1062

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T22:27:44.483036Z digest=sha256:b925ac43ea58172b9e7fe96bf30f3b593fcf4199d8fc8d8c447ca1053a81208c

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T22:27:44.486765Z digest=sha256:c3444e539fe66f703c1893629872a671622d6f8d0ffdeb451f7ef303bbd8450e

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=sha256:54f11bd622b11334a56c420ae4dd50c0b2fd8d795fee29765a1a2441c519fbd7

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T22:27:44.476034Z digest=sha256:aef66ced44c28c444863548e84ee94835e8e6b8a990e3d1a8123a75b645080ef

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T22:27:44.449289Z digest=sha256:bc3d84d3e489fffb448d69804233e753397f56437e6e7149246482a0ae4a34a7

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T22:27:44.457425Z digest=sha256:08389d0352b0ba9ea301c8c28023572600ef8e9ff6e6c2e4ce1d41b8bfac7973

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T22:27:44.453562Z digest=sha256:0b0d452e67bd5e67a0eeb1343c88d02751f8aad8e170e0a4e42f70fc9ab06d18

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 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T22:27:44.479234Z digest=sha256:0aa161e0ffaaa636da5fad8f587ce9fd679dce20db5cac31421cda0a8f0c9c6a

Pith citing papers

No inbound Pith citation observations are available.