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Paper Citation Record · LEDGER

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings

As of 19 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2504.14766.

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

pith.paper-citation-record.v1
2504.14766 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:47:49.740120Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

39 of 39 outbound references displayed

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  • verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bf0543cf-1a56-4c4b-88ce-c63a5ee448a8 · outbound

This paper cites online" 'onlinestring :=.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings online" 'onlinestring :=

Reference 1

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Observation 95a18615-bc7c-4e1e-8a75-6185cd8a885f · outbound

This paper cites write newline.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings write newline

Reference 2

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Observation 83b3227f-15fe-4cac-ad30-ad597c0a3cde · outbound

This paper cites Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks

Reference 3

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Observation 5edad820-7bc7-46b7-98bc-b5fac0d288ae · outbound

This paper cites On the Pitfalls of Analyzing Individual Neurons in Language Models.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings On the Pitfalls of Analyzing Individual Neurons in Language Models

Reference 4

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Unresolved cited work

Reference 5

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Unresolved cited work

Reference 6

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This paper cites Crafting Interpretable Embeddings by Asking LLMs Questions.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Crafting Interpretable Embeddings by Asking LLMs Questions

Reference 7

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Observation dd3896f9-58fc-46aa-be6d-09e6132e5f83 · outbound

This paper cites Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Reference 8

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Observation 597f102c-94a7-4b6b-8065-683fc6f77446 · outbound

This paper cites Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Recent advances in text embedding: A Comprehensive Review of Top-Performing Methods on the MTEB Benchmark

Reference 9

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This paper cites What you can cram into a single vector: Probing sentence embeddings for linguistic properties.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings What you can cram into a single vector: Probing sentence embeddings for linguistic properties

Reference 10

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Unresolved cited work

Reference 11

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 12

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This paper cites Discovering Salient Neurons in Deep NLP Models.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Discovering Salient Neurons in Deep NLP Models

Reference 13

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Unresolved cited work

Reference 14

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Unresolved cited work

Reference 15

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Observation e90935e8-f494-4da4-b1d4-30b2deb9d1dd · outbound

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Sparse Overcomplete Word Vector Representations

Reference 16

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Unresolved cited work

Reference 17

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Finding Neurons in a Haystack: Case Studies with Sparse Probing

Reference 18

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Unresolved cited work

Reference 19

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Reference 20

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings The Mythos of Model Interpretability

Reference 21

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Reference 22

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Efficient Estimation of Word Representations in Vector Space

Reference 23

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Unresolved cited work

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Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Unresolved cited work

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-16T11:47:49.735907Z digest=sha256:5be8fc9d86bfc35e576a66005b240d84fc3ca6d79ff7310a7c3621004586f0e9

Observation 20d981b7-2f80-45bf-8d4b-638e8a43c849 · outbound

This paper cites Clinical Concept Extraction with Contextual Word Embedding.

Disentangling Linguistic Features with Dimension-Wise Analysis of Vector Embeddings Clinical Concept Extraction with Contextual Word Embedding

Reference 39

Resolution
metadata mismatch
local_arxiv, observed 2026-08-16T11:47:49.899765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-16T11:47:49.740120Z digest=sha256:966e52fbae9b538447395d1ea4b95310c1b22436967e68db1f7f3d3ebc1da042

Pith citing papers

No inbound Pith citation observations are available.