Paper Citation Record · LEDGER

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs

As of 18 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 1 inbound Pith citation observation for arXiv:2505.17140.

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

pith.paper-citation-record.v1
2505.17140 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:07:32.888385Z

measured 20 of 20 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-18T00:56:28.910819Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T01:00:34.708649Z

Reference resolution

19 of 19 outbound references displayed

verified exact0
verified fuzzy5
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a58146e2-ec41-461e-8200-f191726c73ba · outbound

This paper cites https://www.prolific.com/.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs https://www.prolific.com/

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:07:34.702123Z

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.

Observation b5cd8269-b356-400d-a131-ec8064cc54b5 · outbound

This paper cites Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:31.574580Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad19ebae-2d5a-4a6e-bb41-d08380558b00 · outbound

This paper cites Continual Pre-training of Language Models.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs Continual Pre-training of Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:31.713925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a4ddd9f-ff7d-4347-937b-ac534e444e6b · outbound

This paper cites The Llama 3 Herd of Models.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs The Llama 3 Herd of Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:31.896347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b9ac368-d500-4f0d-bf0f-e3292856fb19 · outbound

This paper cites Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs Injecting New Knowledge into Large Language Models via Supervised Fine-Tuning

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:31.960277Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 95d186f0-80be-4b6a-9347-456106760691 · outbound

This paper cites GPT-4 Technical Report.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs GPT-4 Technical Report

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:32.037281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e0c5fe0a-36aa-48b9-bd4b-6ea2d92f8736 · outbound

This paper cites In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 237–250, Mi- ami, Florida, USA.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 237–250, Mi- ami, Florida, USA

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:07:34.165447Z

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.

Observation 7868846d-a105-434f-a6d2-70e546ea2219 · outbound

This paper cites Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:32.364482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f9653f9-ea68-44a9-81a0-6f4b93817d8d · outbound

This paper cites memorization in large language models.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs memorization in large language models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:32.451606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38e63713-0249-4266-a614-bf1ff6f34fae · outbound

This paper cites an unresolved cited work.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-07T15:07:33.973818Z

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.

Observation 6754f943-9dc2-4a2d-8efe-874d8a93e1b5 · outbound

This paper cites In Proceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 21456–21473, Miami, Florida, USA.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs In Proceed- ings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 21456–21473, Miami, Florida, USA

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:07:33.819319Z

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.

Observation 28822242-6eb1-4640-8529-d44572194410 · outbound

This paper cites Enhancing LLM Knowledge Learning through Generalization.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs Enhancing LLM Knowledge Learning through Generalization

Reference 18

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T15:07:33.134216Z

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.

Observation b83d8232-9e43-4aee-b455-4555ab1dd2c0 · outbound

This paper cites • Swing states included Wisconsin, Michigan, Pennsylvania, Arizona, Georgia, Nevada, and North Carolina, all won by Trump.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs • Swing states included Wisconsin, Michigan, Pennsylvania, Arizona, Georgia, Nevada, and North Carolina, all won by Trump

Reference 2004

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:07:33.673870Z

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.

Observation bfc8649f-e3b4-4b50-a0c6-e1630714c53a · outbound

This paper cites Scaling Laws for Neural Language Models.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs Scaling Laws for Neural Language Models

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:31.614052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93c57e25-385a-4a75-ac63-d6b4fd5ad82c · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:31.802378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e653aef-2ef9-4f7f-8ec3-fd9a2ee1dae0 · outbound

This paper cites Training language models to follow instructions with human feedback.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs Training language models to follow instructions with human feedback

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:32.129802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68be74cf-1d57-4e77-8963-6449e37ff769 · outbound

This paper cites In Findings of the Asso- ciation for Computational Linguistics: EACL 2023 , pages 1856–1869, Dubrovnik, Croatia.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs In Findings of the Asso- ciation for Computational Linguistics: EACL 2023 , pages 1856–1869, Dubrovnik, Croatia

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:07:34.399910Z

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.

Observation 1097d4fe-e750-4bbb-bb98-d8b10423bc94 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:31.406902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d3a84c9-21eb-42ad-8877-89c86ed6047a · outbound

This paper cites Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks.

Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs Toward Generalizable Evaluation in the LLM Era: A Survey Beyond Benchmarks

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T15:07:31.448259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 9369ee9b-f119-4d2c-81eb-13dee09df2ae · inbound

LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs cites this paper.

LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs Data Doping or True Intelligence? Evaluating the Transferability of Injected Knowledge in LLMs

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-18T01:00:34.711485Z

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.