Paper Citation Record · LEDGER

Prompting GPT-3 To Be Reliable

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2210.09150.

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

pith.paper-citation-record.v1
2210.09150 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:12:24.224762Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T05:16:39.646569Z

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 8b567fdd-b89b-4864-8e37-b8fa6b5df2d6 · inbound

REPLUG: Retrieval-Augmented Black-Box Language Models cites this paper.

REPLUG: Retrieval-Augmented Black-Box Language Models Prompting GPT-3 To Be Reliable

Reference 10

Resolution: metadata mismatch; arxiv_id, observed 2026-05-17T12:41:54.045523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 38aae688-1d41-4b14-ac27-f1708a89177c · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models Prompting GPT-3 To Be Reliable

Reference 175

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:28:39.559717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 874910e9-8622-4fa1-a696-2f4c0ec2c1e2 · inbound

Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment cites this paper.

Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment Prompting GPT-3 To Be Reliable

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-17T22:30:44.785699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 499a1ac1-3f3b-452d-a716-a68e583045b3 · inbound

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines cites this paper.

DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines Prompting GPT-3 To Be Reliable

Reference 51

Resolution: verified exact; arxiv_id, observed 2026-05-11T18:57:47.092936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ef529087-85b5-4950-ac20-0c215cf086f2 · inbound

A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models cites this paper.

A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models Prompting GPT-3 To Be Reliable

Reference 47

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T19:15:13.369755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 99c86017-6745-4a4d-935a-2c35e05ab2d2 · inbound

Relative Bias: A Comparative Framework for Quantifying Bias in LLMs cites this paper.

Relative Bias: A Comparative Framework for Quantifying Bias in LLMs Prompting GPT-3 To Be Reliable

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:12:24.224762Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad418641-8322-49e2-a88d-de9ed0917718 · inbound

How Knowledge Popularity Influences and Enhances LLM Knowledge Boundary Perception cites this paper.

How Knowledge Popularity Influences and Enhances LLM Knowledge Boundary Perception Prompting GPT-3 To Be Reliable

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:51:10.042586Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 731ef2ee-cd2e-4f3d-8c17-f6f6204c6eb0 · inbound

Novobo: Supporting Teachers' Peer Learning of Instructional Gestures by Teaching a Mentee AI-Agent Together cites this paper.

Novobo: Supporting Teachers' Peer Learning of Instructional Gestures by Teaching a Mentee AI-Agent Together Prompting GPT-3 To Be Reliable

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:57.778696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6584fbb1-7223-44f4-93b2-ae25161c6867 · inbound

Advertising in AI systems: Society must be vigilant cites this paper.

Advertising in AI systems: Society must be vigilant Prompting GPT-3 To Be Reliable

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:31.134018Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29d595ed-24fc-4398-951b-76a0994dc815 · inbound

S2LPP: Small-to-Large Prompt Prediction across LLMs cites this paper.

S2LPP: Small-to-Large Prompt Prediction across LLMs Prompting GPT-3 To Be Reliable

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T14:04:18.510086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3b08277-d086-484a-ae09-b0862318a9e9 · inbound

Learning Uncertainty from Sequential Internal Dispersion in Large Language Models cites this paper.

Learning Uncertainty from Sequential Internal Dispersion in Large Language Models Prompting GPT-3 To Be Reliable

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-10T08:48:02.456478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c80ec249-b028-4f40-b456-9b65d0ec2d98 · inbound

Can LLM Rerankers Predict Their Own Ranking Performance? cites this paper.

Can LLM Rerankers Predict Their Own Ranking Performance? Prompting GPT-3 To Be Reliable

Reference 42

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T05:16:39.648028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 72542388-f54e-4868-86fe-8e54ab3e0083 · inbound

Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs'Hallucinations cites this paper.

Knowledge Injection Exists in MoE? Exploring Expert-Aware Contrast Decoding in MoE for Mitigating LLMs'Hallucinations Prompting GPT-3 To Be Reliable

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T14:37:30.373308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.