Paper Citation Record · LEDGER

Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2404.03921.

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

pith.paper-citation-record.v1
2404.03921 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:29:59.787666Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T13:59:03.763348Z

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 0e4af01b-a811-4372-95d5-3d4d87bb800f · inbound

HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning with Hard Negatives cites this paper.

HNCSE: Advancing Sentence Embeddings via Hybrid Contrastive Learning with Hard Negatives Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T17:55:23.912827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 690fb787-8cbe-455d-95d4-5252b8001553 · inbound

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs cites this paper.

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T14:53:05.986519Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6423431-95bd-4497-8624-5ab60700933d · inbound

LLMs are Also Effective Embedding Models: An In-depth Overview cites this paper.

LLMs are Also Effective Embedding Models: An In-depth Overview Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models

Reference 208

Resolution: verified exact; local_arxiv, observed 2026-08-11T13:59:03.766645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 46e0b899-d5b2-4e59-ad1d-ff59339cae88 · inbound

Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering cites this paper.

Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T20:29:59.787666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.