Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Fine-tuning Smaller Language Models for Question Answering over Financial Documents
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2408.12337.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:30:50.234543Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T12:43:47.103062Z
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 938b0371-4e26-4bdd-a144-bd1136ac483e · inbound
Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations cites this paper.
Enhancing Financial VQA in Vision Language Models using Intermediate Structured Representations Fine-tuning Smaller Language Models for Question Answering over Financial Documents
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:30:50.234543Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dcd02dff-623b-4df1-84aa-1832b0416982 · inbound
Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization cites this paper.
Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Fine-tuning Smaller Language Models for Question Answering over Financial Documents
Reference 26
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T12:43:47.149300Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.