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Paper Citation Record · LEDGER

Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.04873.

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

pith.paper-citation-record.v1
2503.04873 v1

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 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:44:20.986905Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:09:46.960678Z

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 f06bcd4e-e559-4187-a971-562015a0f505 · inbound

Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion cites this paper.

Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?

Reference 80

Resolution
unresolved
no resolver link, observed 2026-08-09T19:44:20.986905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T19:44:20.986905Z digest=sha256:39944d858b592b6e0f76f5d7ac27271d11b7f533c31887a43bd83fb6524be857

Observation 33f96c4b-a84f-49ca-980b-b38f54ff3144 · inbound

MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning cites this paper.

MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning Are Large Language Models Good In-context Learners for Financial Sentiment Analysis?

Reference 29

Resolution
verified exact
local_arxiv, observed 2026-08-07T15:09:47.080129Z

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.

source=pdf_text observed=2026-08-07T15:09:45.845081Z digest=sha256:9f23cf2a96e987c3ea1b3cef190493bd1bbf01cd3a4afc13e46a353ba9c78f4e