Paper Citation Record · LEDGER

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling

As of 8 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2507.14735.

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

pith.paper-citation-record.v1
2507.14735 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:54:24.135142Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact2
verified fuzzy15
unresolved18
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 344831fa-ae3b-4517-a73f-1d323a9d348c · outbound

This paper cites an unresolved cited work.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Unresolved cited work

Reference 1

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Observation 8f675118-14a6-4677-8636-8916e03d271b · outbound

This paper cites In: Proceedings of the 33rd International Conference on Software Engineering.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: Proceedings of the 33rd International Conference on Software Engineering

Reference 2

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Observation cb37af80-311c-4b1b-81d0-cd89499aba5d · outbound

This paper cites In: 2024 International Joint Conference on Neural Networks (IJCNN).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: 2024 International Joint Conference on Neural Networks (IJCNN)

Reference 3

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Observation d2b91ee5-1df4-480e-90e0-d109c105da9c · outbound

This paper cites Optimizing Large Language Model Hyperparameters for Code Generation.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Optimizing Large Language Model Hyperparameters for Code Generation

Reference 4

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This paper cites In: 2023 ACM/IEEE Interna- tional Conference on Model Driven Engineering Languages and Systems Compan- ion (MODELS-C).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: 2023 ACM/IEEE Interna- tional Conference on Model Driven Engineering Languages and Systems Compan- ion (MODELS-C)

Reference 5

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This paper cites In: Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: Proceedings of the ACM/IEEE 19th International Conference on Model Driven Engineering Languages and Systems

Reference 6

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Observation 3f80515f-2a57-4d30-bda5-f7d23308cd33 · outbound

This paper cites an unresolved cited work.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Unresolved cited work

Reference 8

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Observation 98d306c0-a13d-4d44-ae46-0f5aca3f1e56 · outbound

This paper cites an unresolved cited work.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Unresolved cited work

Reference 9

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Observation 3b6e13b0-4cb1-44c5-b097-25ffa4cd28c2 · outbound

This paper cites ACM Trans.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling ACM Trans

Reference 10

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Observation 76377c1a-a6c8-4da9-8dec-0140a313ab0d · outbound

This paper cites Software and Systems Modeling 22(3), 781–793 (Jun 2023).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Software and Systems Modeling 22(3), 781–793 (Jun 2023)

Reference 11

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This paper cites In: Proceedings of the 45th Inter- national Conference on Software Engineering: New Ideas and Emerging Results.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: Proceedings of the 45th Inter- national Conference on Software Engineering: New Ideas and Emerging Results

Reference 12

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Observation aa750678-92dc-42ae-b993-11e8cc0122b1 · outbound

This paper cites In: 2023 ACM/IEEE 26th International Conference on Model Driven En- gineering Languages and Systems (MODELS).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: 2023 ACM/IEEE 26th International Conference on Model Driven En- gineering Languages and Systems (MODELS)

Reference 13

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Observation 00267bcd-760f-4c8f-a170-9319864c39c0 · outbound

This paper cites In: Meila, M., Zhang, T.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: Meila, M., Zhang, T

Reference 14

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Observation 402dcfc6-23c8-4031-b7c5-70721cfb4ef7 · outbound

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Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Unresolved cited work

Reference 15

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Observation 1d72d785-b438-4ccd-95b3-c0e5320e72d1 · outbound

This paper cites In: Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: Proceedings of the 18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

Reference 16

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Observation 23af9ec0-17ce-4d07-be2b-b8fbf51c8284 · outbound

This paper cites IEEE Trans.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling IEEE Trans

Reference 17

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Observation a55d5917-2838-4510-b79e-59d734d95656 · outbound

This paper cites Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B

Reference 18

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Observation 9659e53e-fc29-412d-9d6e-6af5b39231d5 · outbound

This paper cites Software and Systems Modeling pp.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Software and Systems Modeling pp

Reference 19

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Observation b63a24f6-736b-4880-adb9-82e7e71f3042 · outbound

This paper cites In: Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems

Reference 20

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Observation 85eb6981-5216-4b6e-b7f2-d6c0d33d5fa9 · outbound

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Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling ACM Trans

Reference 21

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Observation 63b04dca-34bb-41d1-be22-2d1b9afc4cc6 · outbound

This paper cites ACM computing surveys (CSUR)28(1), 77–80 (1996).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling ACM computing surveys (CSUR)28(1), 77–80 (1996)

Reference 22

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Observation 70e20c15-c7f7-4443-a81a-b3bcc2c6667d · outbound

This paper cites Evolu- tionary Computation 22(4), 651–678 (2014).https://doi.org/10.1162/EVCO_a_ 00128.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Evolu- tionary Computation 22(4), 651–678 (2014).https://doi.org/10.1162/EVCO_a_ 00128

Reference 23

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Observation 2f50fa95-a996-491f-bffe-1329fe074eba · outbound

This paper cites an unresolved cited work.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Unresolved cited work

Reference 24

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Source-reported events for the cited work

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Observation 413447c3-acc8-4c0b-a2a3-8287d2cb0500 · outbound

This paper cites In: Adaptive and Natural Computing Algorithms: 8th Interna- tional Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceed- ings, Part I 8.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: Adaptive and Natural Computing Algorithms: 8th Interna- tional Conference, ICANNGA 2007, Warsaw, Poland, April 11-14, 2007, Proceed- ings, Part I 8

Reference 25

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Observation 428c856d-4d5e-4b54-84dd-bf5b46c793b3 · outbound

This paper cites Applied Soft Computing133, 109916 (Jan 2023).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Applied Soft Computing133, 109916 (Jan 2023)

Reference 26

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Observation 77332d25-0aa8-4195-9adf-90c7cc8de8f9 · outbound

This paper cites In- ternational Journal of Computer Applications162(10) (2017).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In- ternational Journal of Computer Applications162(10) (2017)

Reference 27

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Observation 65b787fe-865e-4cf5-905d-898f0aea1673 · outbound

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Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: Proceedings of the 34th Interna- tional Conference on Neural Information Processing Systems

Reference 28

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Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Software and Systems Modeling pp

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Observation b4ef8b7e-4461-47ac-813e-4793e5162df3 · outbound

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Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Reference 30

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Observation cc86435b-ca35-4610-bd85-72415be8b3a5 · outbound

This paper cites meta.com/llama3/.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling meta.com/llama3/

Reference 31

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Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Software and Systems Modeling19(5), 1045– 1053 (Sep 2020).https://doi.org/10.1007/s10270-020-00814-5, http://link

Reference 32

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Observation 0556c718-d1e6-4912-9a1e-80d7bc762803 · outbound

This paper cites Pearson Higher Education (2004).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Pearson Higher Education (2004)

Reference 33

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Observation 49102a45-f037-41e9-a987-02f4c7120351 · outbound

This paper cites In: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings

Reference 34

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Observation 1ab519d0-1162-4092-8f3e-6a439f584a13 · outbound

This paper cites Information processing & management24(5), 513–523 (1988).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Information processing & management24(5), 513–523 (1988)

Reference 35

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Observation 7188577c-ebfb-49cf-bb0f-fde746a320f3 · outbound

This paper cites In: Proceedings of the 38th International Conference on Software Engineering.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling In: Proceedings of the 38th International Conference on Software Engineering

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T15:54:23.770712Z

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Observation 78f7ba41-4c70-4118-99da-35ce9ff79446 · outbound

This paper cites Journal of Educational and Behavioral Statistics 25(2), 101–132 (2000).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Journal of Educational and Behavioral Statistics 25(2), 101–132 (2000)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:54:27.083252Z

Source-reported events for the cited work

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Observation 9e600a39-3aa1-442f-9ed0-620edb4f8845 · outbound

This paper cites Advances in neural information processing systems35, 24824–24837 (2022) Investigating LLMs Tuning and Prompt Engineering for Domain Modeling 19.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Advances in neural information processing systems35, 24824–24837 (2022) Investigating LLMs Tuning and Prompt Engineering for Domain Modeling 19

Reference 38

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Observation 330b9438-a7d8-49be-aadd-46d812a9990d · outbound

This paper cites Software and Systems Modeling pp.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Software and Systems Modeling pp

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:54:26.703981Z

Source-reported events for the cited work

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Observation 58a52c0f-8f13-4016-9098-6ed937290e1c · outbound

This paper cites Springer Berlin Heidelberg, Berlin, Heidelberg (2012).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Springer Berlin Heidelberg, Berlin, Heidelberg (2012)

Reference 40

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Source-reported events for the cited work

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Observation 8c0f7971-2b53-45dc-a5a8-a104f5c241bd · outbound

This paper cites Encyclopedia of Biostatistics8 (2005).

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling Encyclopedia of Biostatistics8 (2005)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T15:54:26.500939Z

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Observation fcb8ab69-4531-4ce3-9204-eea483c828af · outbound

This paper cites BERTScore: Evaluating Text Generation with BERT.

Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling BERTScore: Evaluating Text Generation with BERT

Reference 42

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Pith citing papers

No inbound Pith citation observations are available.