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Paper Citation Record · LEDGER

Fairness-guided Few-shot Prompting for Large Language Models

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2303.13217.

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

pith.paper-citation-record.v1
2303.13217 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:21:54.580676Z

measured 1 of 1 external citation measurements

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

24
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 fee61e28-e7f4-41dc-8be8-44ac5957afb5 · inbound

WizardLM: Empowering large pre-trained language models to follow complex instructions cites this paper.

WizardLM: Empowering large pre-trained language models to follow complex instructions Fairness-guided Few-shot Prompting for Large Language Models

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-13T07:28:24.921177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-05-13T07:28:24.827546Z digest=sha256:28ac04208bd79e60ab08bf52e413fa4715cf1deb2b57a400cb9ecc451c88567a

Observation 36916899-7c8b-402c-954f-c0e3bb460c46 · inbound

Time Will Tell: Timing Side Channels via Output Token Count in Large Language Models cites this paper.

Time Will Tell: Timing Side Channels via Output Token Count in Large Language Models Fairness-guided Few-shot Prompting for Large Language Models

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T11:29:31.038713Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:29:31.038713Z digest=sha256:2fc558bd58916e9fca99fcbb78cb06bf8058a9884bdad0db9560d8bef3e2b9f1

Observation 6ba1ace2-7f62-4a33-82e8-66bc5957af9c · inbound

Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models cites this paper.

Towards a Unified Paradigm: Integrating Recommendation Systems as a New Language in Large Models Fairness-guided Few-shot Prompting for Large Language Models

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-11T06:02:36.467984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T06:02:36.467984Z digest=sha256:ad3a21bdca9373141dd9bc88e09567f1a754fc85010b272c8b8379b45ef8ead3

Observation bfa7ad71-842f-4722-aa11-30e138d8fd74 · inbound

Addressing speaker gender bias in large scale speech translation systems cites this paper.

Addressing speaker gender bias in large scale speech translation systems Fairness-guided Few-shot Prompting for Large Language Models

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-10T21:10:24.790968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T21:10:24.790968Z digest=sha256:af3c07bf0bc3cc876345a9bc2ad570edc0a1993256c41f562b895a754070c7d7

Observation a6bc3c8e-7287-466e-ad32-df1a8d213390 · inbound

StyleAdaptedLM: Enhancing Instruction Following Models with Efficient Stylistic Transfer cites this paper.

StyleAdaptedLM: Enhancing Instruction Following Models with Efficient Stylistic Transfer Fairness-guided Few-shot Prompting for Large Language Models

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-15T18:21:54.580676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T18:21:54.580676Z digest=sha256:1ae1e5047b03080364fde60b540f9f6a7da79e03ef1a977b0da46a6e0c384246

Observation 9b41ae8e-db4d-4e9f-a1a6-867148eda445 · inbound

Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval cites this paper.

Bias Mitigation Agent: Optimizing Source Selection for Fair and Balanced Knowledge Retrieval Fairness-guided Few-shot Prompting for Large Language Models

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-05T16:21:17.903310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:21:17.903310Z digest=sha256:40978ef4d532939ea30b6d54bad1668845fd56e5f433ce29741c759295cf4d05

Observation 234dcfe9-616d-4623-9fc5-534f4d7f66d1 · inbound

IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling cites this paper.

IoT-Brain: Grounding LLMs for Semantic-Spatial Sensor Scheduling Fairness-guided Few-shot Prompting for Large Language Models

Reference 46

Resolution
verified exact
arxiv_id, observed 2026-05-11T06:05:56.815584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-10T17:49:16.409834Z digest=sha256:6c435d74bc9757e9df3cfff43cee135aa397d068ae30c060f97b649b540dbd72

Observation 101bf889-b0a5-49d0-a3a8-f793bb1a4c25 · inbound

OOPrompt: Reifying Intents into Structured Artifacts for Modular and Iterative Prompting cites this paper.

OOPrompt: Reifying Intents into Structured Artifacts for Modular and Iterative Prompting Fairness-guided Few-shot Prompting for Large Language Models

Reference 22

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:01:05.255219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-05-10T02:29:16.582341Z digest=sha256:f85e6d634ddc037802b4f55900656c711b44a75e6551217a66557f5925a6222c

Observation aeb2f1ef-25b8-4733-9456-04b105aad3e0 · inbound

Toward Calibrated, Fair, and accurate Deepfake Detection cites this paper.

Toward Calibrated, Fair, and accurate Deepfake Detection Fairness-guided Few-shot Prompting for Large Language Models

Reference 152

Resolution
verified exact
arxiv_id, observed 2026-06-28T07:11:45.238578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-06-28T07:05:18.026601Z digest=sha256:78f9b691416cfd940929a0448927f6474adcd9b170c4a220550fe5ed0aecdc99