Paper Citation Record · LEDGER

Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2103.15543.

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

pith.paper-citation-record.v1
2103.15543 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:25:31.982926Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T08:13:15.119954Z

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 53824f08-2337-4e71-8e59-72ae79a05971 · inbound

Neutralizing Backdoors through Information Conflicts for Large Language Models cites this paper.

Neutralizing Backdoors through Information Conflicts for Large Language Models Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-12T11:25:31.982926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd554d48-d0c8-413f-97f0-70be8e6872aa · inbound

A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations cites this paper.

A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T00:50:00.172135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 264f9ed3-e824-459d-9bde-18763e4fc933 · inbound

Can You Trust the Vectors in Your Vector Database? Black-Hole Attack from Embedding Space Defects cites this paper.

Can You Trust the Vectors in Your Vector Database? Black-Hole Attack from Embedding Space Defects Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:40:48.491654Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0025866a-9dbc-4b30-8bed-19cbfe76cea1 · inbound

Re-Triggering Safeguards within LLMs for Jailbreak Detection cites this paper.

Re-Triggering Safeguards within LLMs for Jailbreak Detection Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:06:25.063283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3accc2d7-3a5d-4de5-a34d-098d56a6c592 · inbound

DocRetriever: A Plug-and-Play Framework for Multimodal Document Retrieval with Comprehensive Benchmark cites this paper.

DocRetriever: A Plug-and-Play Framework for Multimodal Document Retrieval with Comprehensive Benchmark Be Careful about Poisoned Word Embeddings: Exploring the Vulnerability of the Embedding Layers in NLP Models

Reference 69

Resolution: verified exact; arxiv_id, observed 2026-06-29T08:13:15.121448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.