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Paper Citation Record · LEDGER

Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2311.07099.

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

pith.paper-citation-record.v1
2311.07099 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:14:24.941998Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T13:00:04.538013Z

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 18bf9a03-0c06-404d-939a-5860fe003878 · inbound

Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement cites this paper.

Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning

Reference 66

Resolution
unresolved
no resolver link, observed 2026-08-07T14:14:24.941998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:14:24.941998Z digest=sha256:ca81394f766f6ac0176a0764423816b2ba3f9e15dc02ba1b60fa04bd908dfb11

Observation d1a9cd3a-ca80-4190-8839-9599945a3e77 · inbound

Can human clinical rationales improve the performance and explainability of clinical text classification models? cites this paper.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-08-06T13:00:04.542592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T13:00:04.163188Z digest=sha256:e89b2fc2d7a507714929fc5cc75a93d752c881290263578a5d1fa596e43da007