Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Large language models effectively leverage document-level context for literary translation, but critical errors persist
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2304.03245.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T04:31:46.234899Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
8
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 35c3642d-3420-45ce-87de-ca08e5879aa8 · inbound
A 2-step Framework for Automated Literary Translation Evaluation: Its Promises and Pitfalls cites this paper.
A 2-step Framework for Automated Literary Translation Evaluation: Its Promises and Pitfalls Large language models effectively leverage document-level context for literary translation, but critical errors persist
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T04:31:46.234899Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b07126fb-ed7a-4290-b884-8dfed7cfbb08 · inbound
LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods cites this paper.
LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods Large language models effectively leverage document-level context for literary translation, but critical errors persist
Reference 108
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T23:08:36.926906Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation f72aa882-cc99-4c18-beca-321996bff9e5 · inbound
Self-Evolution Knowledge Distillation for LLM-based Machine Translation cites this paper.
Self-Evolution Knowledge Distillation for LLM-based Machine Translation Large language models effectively leverage document-level context for literary translation, but critical errors persist
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T11:59:54.841882Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cfeed4c6-7a8c-4242-9d10-eba075ee4e74 · inbound
Psychology-Driven Enhancement of Humour Translation cites this paper.
Psychology-Driven Enhancement of Humour Translation Large language models effectively leverage document-level context for literary translation, but critical errors persist
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:04:30.990346Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6718c6bf-869c-4b07-a343-76d0d4db2d49 · inbound
ACE-Bench: A Lightweight Benchmark for Evaluating Azure SDK Usage Correctness cites this paper.
ACE-Bench: A Lightweight Benchmark for Evaluating Azure SDK Usage Correctness Large language models effectively leverage document-level context for literary translation, but critical errors persist
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T23:06:50.977008Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.