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Paper Citation Record · LEDGER

Simple Techniques for Enhancing Sentence Embeddings in Generative Language Models

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 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 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:53:05.986519Z

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 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.

source=pdf_text observed=2026-08-11T14:53:05.986519Z digest=sha256:bbebb8f86a62fc897df74ee5ea57405f65b0fd670d20602e2b46d99b9145418c

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 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-11T13:59:02.046832Z digest=sha256:879f9b224729a630549fab7629199c1fe05d0c0e0717a77d6f447b1435a1a5ae