Paper Citation Record · LEDGER

Benchmarking Prompt Sensitivity in Large Language Models

As of 9 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 3 inbound Pith citation observations for arXiv:2502.06065.

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

pith.paper-citation-record.v1
2502.06065 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:55:45.557734Z

measured 44 of 44 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T13:40:04.275438Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T13:43:19.601845Z

Reference resolution

41 of 41 outbound references displayed

verified exact5
verified fuzzy11
unresolved23
parse uncertain0
malformed identifier2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c7831059-049e-483c-b6a7-5ae62f7a32e5 · outbound

This paper cites an unresolved cited work.

Benchmarking Prompt Sensitivity in Large Language Models Unresolved cited work

Reference 1

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Observation f0db9cb2-d921-4d21-9879-cb757f49591f · outbound

This paper cites an unresolved cited work.

Benchmarking Prompt Sensitivity in Large Language Models Unresolved cited work

Reference 2

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Observation 5be17900-1104-40a4-9899-7b9681e98a42 · outbound

This paper cites In: Al- Onaizan,Y.,Bansal,M.,Chen,Y.N.(eds.)Proceedingsofthe2024ConferenceonEmpirical Methods in Natural Language Processing.

Benchmarking Prompt Sensitivity in Large Language Models In: Al- Onaizan,Y.,Bansal,M.,Chen,Y.N.(eds.)Proceedingsofthe2024ConferenceonEmpirical Methods in Natural Language Processing

Reference 3

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Observation 2eea4f62-aa24-40e7-8473-e1da6528e81f · outbound

This paper cites In: European Conference on Information Retrieval.

Benchmarking Prompt Sensitivity in Large Language Models In: European Conference on Information Retrieval

Reference 4

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This paper cites In: Proceedings of the 2024 AnnualInternationalACMSIGIRConferenceonResearchandDevelopmentinInformation Retrieval in the Asia Pacific Region.

Benchmarking Prompt Sensitivity in Large Language Models In: Proceedings of the 2024 AnnualInternationalACMSIGIRConferenceonResearchandDevelopmentinInformation Retrieval in the Asia Pacific Region

Reference 5

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Observation afea40b4-363b-46dc-9477-8eb6156e6d6b · outbound

This paper cites In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management.

Benchmarking Prompt Sensitivity in Large Language Models In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Reference 6

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Observation 7540a4d6-a0e8-4039-9ebf-3ebec2efc0df · outbound

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Benchmarking Prompt Sensitivity in Large Language Models Unresolved cited work

Reference 7

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This paper cites an unresolved cited work.

Benchmarking Prompt Sensitivity in Large Language Models Unresolved cited work

Reference 8

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Observation ca08d4bd-05fa-42ca-88c8-50f68234115a · outbound

This paper cites In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management.

Benchmarking Prompt Sensitivity in Large Language Models In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Reference 9

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Observation 5104d4e0-1104-4669-b20e-731c025fe304 · outbound

This paper cites What's the Magic Word? A Control Theory of LLM Prompting.

Benchmarking Prompt Sensitivity in Large Language Models What's the Magic Word? A Control Theory of LLM Prompting

Reference 10

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Observation abc38505-6812-43a0-a97b-1d0c66d75fdb · outbound

This paper cites In: European Conference on Information Retrieval.

Benchmarking Prompt Sensitivity in Large Language Models In: European Conference on Information Retrieval

Reference 11

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Observation 522b75aa-f2cc-49d0-92e0-c56cc7f0eea2 · outbound

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Benchmarking Prompt Sensitivity in Large Language Models Unresolved cited work

Reference 12

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Observation 73b7a0a9-616b-40b3-b352-2b02ab9cff01 · outbound

This paper cites In: European Conference on Information Retrieval.

Benchmarking Prompt Sensitivity in Large Language Models In: European Conference on Information Retrieval

Reference 13

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Observation 0737b338-7258-4e24-833d-e987613140a4 · outbound

This paper cites In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Informa- tion Retrieval.

Benchmarking Prompt Sensitivity in Large Language Models In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Informa- tion Retrieval

Reference 14

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Observation fb873f31-db0a-42d2-b04a-62f139adcc0c · outbound

This paper cites Unveiling and Manipulating Prompt Influence in Large Language Models.

Benchmarking Prompt Sensitivity in Large Language Models Unveiling and Manipulating Prompt Influence in Large Language Models

Reference 15

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Observation 47e15cfe-6d79-4e2d-ba23-a921e1f3f21b · outbound

This paper cites Information13(2), 83 (2022).

Benchmarking Prompt Sensitivity in Large Language Models Information13(2), 83 (2022)

Reference 16

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Observation fb4d5384-3382-4e8a-8f76-1a495dcddcc6 · outbound

This paper cites IEEE Access11, 76581–76604 (2023).

Benchmarking Prompt Sensitivity in Large Language Models IEEE Access11, 76581–76604 (2023)

Reference 17

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Observation ba3f28d9-862f-4301-8967-b5991ef90d38 · outbound

This paper cites In: CIKM (2008).

Benchmarking Prompt Sensitivity in Large Language Models In: CIKM (2008)

Reference 18

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Observation e50f2947-2f99-4298-a5e9-46bda323f118 · outbound

This paper cites In: Proceedings of the 33rd ACM International Confer- ence on Information and Knowledge Management.

Benchmarking Prompt Sensitivity in Large Language Models In: Proceedings of the 33rd ACM International Confer- ence on Information and Knowledge Management

Reference 19

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Observation 1108b83d-6dbb-42b0-8f06-b3c492a20636 · outbound

This paper cites Mistral 7B.

Benchmarking Prompt Sensitivity in Large Language Models Mistral 7B

Reference 20

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Observation 26b35b12-ec95-466d-8789-17d125c14868 · outbound

This paper cites In: Barzilay, R., Kan, M.Y.

Benchmarking Prompt Sensitivity in Large Language Models In: Barzilay, R., Kan, M.Y

Reference 21

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Observation 5d2ef4fe-0748-4656-be65-2175709938e6 · outbound

This paper cites In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

Benchmarking Prompt Sensitivity in Large Language Models In: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Reference 22

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Observation 3b7ffde7-27fd-4fc6-b0bb-735a7538e3a7 · outbound

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Benchmarking Prompt Sensitivity in Large Language Models Unresolved cited work

Reference 23

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Observation b729d661-c7d7-4087-9793-392ae8c4b0ea · outbound

This paper cites Internet Reference Services Quarterly27, 203 – 210 (2023).https://doi.org/10.1080/ 10875301.2023.2227621.

Benchmarking Prompt Sensitivity in Large Language Models Internet Reference Services Quarterly27, 203 – 210 (2023).https://doi.org/10.1080/ 10875301.2023.2227621

Reference 24

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This paper cites https://doi.org/10.18653/v1/2023.findings-emnlp.241, http: //dx.doi.org/10.18653/v1/2023.findings-emnlp.241.

Benchmarking Prompt Sensitivity in Large Language Models https://doi.org/10.18653/v1/2023.findings-emnlp.241, http: //dx.doi.org/10.18653/v1/2023.findings-emnlp.241

Reference 25

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Observation e257f84d-e8bb-43cd-9f61-7491da07ad2c · outbound

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Benchmarking Prompt Sensitivity in Large Language Models Unresolved cited work

Reference 26

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Observation d4604f86-0b0a-4cca-890d-2db2442aa080 · outbound

This paper cites Query Performance Prediction using Relevance Judgments Generated by Large Language Models.

Benchmarking Prompt Sensitivity in Large Language Models Query Performance Prediction using Relevance Judgments Generated by Large Language Models

Reference 27

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Observation 93cfb014-0c4f-48aa-a81e-36368b23297d · outbound

This paper cites The Llama 3 Herd of Models.

Benchmarking Prompt Sensitivity in Large Language Models The Llama 3 Herd of Models

Reference 28

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Observation 4639ae2e-21b8-4fb9-ab6f-ff2898196f44 · outbound

This paper cites Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science.

Benchmarking Prompt Sensitivity in Large Language Models Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science

Reference 29

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Observation 5f4fe807-9dfd-41fe-8f26-d703dddce000 · outbound

This paper cites Testing LLMs on Code Generation with Varying Levels of Prompt Specificity.

Benchmarking Prompt Sensitivity in Large Language Models Testing LLMs on Code Generation with Varying Levels of Prompt Specificity

Reference 30

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Observation 167f177b-1e6d-426a-84e8-7a3332fc9d1f · outbound

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Benchmarking Prompt Sensitivity in Large Language Models PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance Prediction

Reference 31

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Observation 1c0b5fbc-294d-4adf-8817-933f07008425 · outbound

This paper cites Semantic Consistency for Assuring Reliability of Large Language Models.

Benchmarking Prompt Sensitivity in Large Language Models Semantic Consistency for Assuring Reliability of Large Language Models

Reference 32

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Observation a3f0f6bb-5e0a-4821-b713-52e4edd0a935 · outbound

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Benchmarking Prompt Sensitivity in Large Language Models In: Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region

Reference 33

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Observation 38e2003d-8cca-4612-b730-81796aadcae7 · outbound

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Benchmarking Prompt Sensitivity in Large Language Models In: Proceedings of the 32nd ACM Inter- national Conference on Information and Knowledge Management

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:55:46.162750Z

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Observation f4ac3d28-1374-40aa-84f8-2b7d1076fb6c · outbound

This paper cites In: European Conference on Information Re- trieval.pp.30–39.Springer(2024).

Benchmarking Prompt Sensitivity in Large Language Models In: European Conference on Information Re- trieval.pp.30–39.Springer(2024)

Reference 35

Resolution: verified exact; doi, observed 2026-08-08T16:55:45.604745Z

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Observation 11dfe6a2-4267-4f6a-ade1-3359f6563496 · outbound

This paper cites Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting.

Benchmarking Prompt Sensitivity in Large Language Models Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting

Reference 36

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Observation 5502d8ec-7031-42d5-88ac-7a32dcfb7701 · outbound

This paper cites In: Proceedings of the 34th International Conference on Neural Information Processing Systems.

Benchmarking Prompt Sensitivity in Large Language Models In: Proceedings of the 34th International Conference on Neural Information Processing Systems

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:55:46.152681Z

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Observation fe230fa3-32ae-4b6d-9ef6-cd5fe05a1870 · outbound

This paper cites eugeneyan.com (Aug 2024),https://eugeneyan.com/writing/llm-evaluators/.

Benchmarking Prompt Sensitivity in Large Language Models eugeneyan.com (Aug 2024),https://eugeneyan.com/writing/llm-evaluators/

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:55:46.142696Z

Source-reported events for the cited work

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Observation d5ffa9f8-6bd1-4e36-88e9-5b82e781e062 · outbound

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Benchmarking Prompt Sensitivity in Large Language Models Unresolved cited work

Reference 39

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Source-reported events for the cited work

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Observation b7267df2-c433-47f8-99f8-1efbc8a7c390 · outbound

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Benchmarking Prompt Sensitivity in Large Language Models PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T16:55:45.553837Z

Source-reported events for the cited work

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Observation 0396fec9-6d6e-4841-9db6-cc8f4f3a90de · outbound

This paper cites ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs.

Benchmarking Prompt Sensitivity in Large Language Models ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs

Reference 41

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 7e8f3c14-1ffe-43e3-98ed-a7d2cb6e2295 · inbound

Understanding the Mechanism of Altruism in Large Language Models cites this paper.

Understanding the Mechanism of Altruism in Large Language Models Benchmarking Prompt Sensitivity in Large Language Models

Reference 192

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Observation ac8129f6-3efa-4cfa-9687-e94cbfea7a27 · inbound

Coordinates of Capability: A Unified MTMM-Geometric Framework for LLM Evaluation cites this paper.

Coordinates of Capability: A Unified MTMM-Geometric Framework for LLM Evaluation Benchmarking Prompt Sensitivity in Large Language Models

Reference 18

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Observation 9eacf9cf-1ef4-420b-bfd1-49665c342223 · inbound

Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits cites this paper.

Stop Drawing Scientific Claims from LLM Social Simulations Without Robustness Audits Benchmarking Prompt Sensitivity in Large Language Models

Reference 54

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Source-reported events for the cited work

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