Paper Citation Record · LEDGER

How Robust are LLMs to In-Context Majority Label Bias?

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2312.16549.

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

pith.paper-citation-record.v1
2312.16549 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 2026-08-21T06:32:19.484+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:24:59.576587Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T17:04:56.390773Z

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 5dd8f1be-d2b1-49e0-bf59-feb24ca057b0 · inbound

The Impact of Example Selection in Few-Shot Prompting on Automated Essay Scoring Using GPT Models cites this paper.

The Impact of Example Selection in Few-Shot Prompting on Automated Essay Scoring Using GPT Models How Robust are LLMs to In-Context Majority Label Bias?

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T10:47:43.803848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5666dd03-3aa8-4fe2-a9a5-e674da4184e3 · inbound

Exploring Imbalanced Annotations for Effective In-Context Learning cites this paper.

Exploring Imbalanced Annotations for Effective In-Context Learning How Robust are LLMs to In-Context Majority Label Bias?

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T23:52:00.861710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 26cd35f1-ff27-4fe3-9998-4d6e690351fe · inbound

Incomplete In-context Learning cites this paper.

Incomplete In-context Learning How Robust are LLMs to In-Context Majority Label Bias?

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T22:24:59.576587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf1d4fdf-6e18-4853-aa96-f9647dd536b0 · inbound

AMEL: Accumulated Message Effects on LLM Judgments cites this paper.

AMEL: Accumulated Message Effects on LLM Judgments How Robust are LLMs to In-Context Majority Label Bias?

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-22T05:11:06.793659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 51eed431-f71f-408f-9891-0905eeb0f13a · inbound

AMEL: Accumulated Message Effects on LLM Judgments cites this paper.

AMEL: Accumulated Message Effects on LLM Judgments How Robust are LLMs to In-Context Majority Label Bias?

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:04:56.392225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.