Paper Citation Record · LEDGER

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts

As of 15 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 3 inbound Pith citation observations for arXiv:2412.15254.

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

pith.paper-citation-record.v1
2412.15254 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:12:04.994801Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T00:49:15.401352Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-09T22:58:13.456014Z

Reference resolution

40 of 40 outbound references displayed

verified exact0
verified fuzzy34
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 19624b89-da81-4060-8417-04ee28b34426 · outbound

This paper cites In: Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering, pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering, pp

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.421708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0d981e1c-cc15-4857-a40f-015b6a3acc3d · outbound

This paper cites ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)18(3), 1–52 (2019).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)18(3), 1–52 (2019)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.396883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 909f609a-122f-455c-a23e-2eb074b58952 · outbound

This paper cites In: Proceedings of the 11th international workshop on semantic evaluation (SEMEVAL-2017), pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: Proceedings of the 11th international workshop on semantic evaluation (SEMEVAL-2017), pp

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.361043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3e3fc021-840f-47f6-9221-9aab69c8b196 · outbound

This paper cites test! Morgan Kaufmann (2020).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts test! Morgan Kaufmann (2020)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.325354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a50e9021-dd51-4c09-897d-ddbb14707b7f · outbound

This paper cites In: Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval), pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval), pp

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.288129Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 59077fac-0147-4e60-a8b2-2d14ec11c23c · outbound

This paper cites MIT press (2003).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts MIT press (2003)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.254843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3a9b305d-fb05-4df4-bfcb-c48cec9c03bd · outbound

This paper cites In: International Conference on Computational Intelligence in Data Science, pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: International Conference on Computational Intelligence in Data Science, pp

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.217156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 25c688c6-cac8-47f9-baf3-114eb32c595d · outbound

This paper cites In: International Conference on Knowledge Science, Engineering and Management, pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: International Conference on Knowledge Science, Engineering and Management, pp

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.185669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 030fec18-c44b-4fd9-8801-77cb26d44df3 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts QLoRA: Efficient Finetuning of Quantized LLMs

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T15:12:04.695269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 697e507e-6caf-45d6-9e9f-4c9334adadba · outbound

This paper cites Applied Linguistics Review14(5), 1451–1473 (2023).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Applied Linguistics Review14(5), 1451–1473 (2023)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.145701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fa9bcbf3-83c2-4562-80c8-3c35bf239c68 · outbound

This paper cites Journal of Artificial Intelligence Research61, 623–698 (2018).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Journal of Artificial Intelligence Research61, 623–698 (2018)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.114120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6a841fec-4016-4592-aad5-593d4fe8cd17 · outbound

This paper cites Journal of Systems and Software 165, 110,570 (2020).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Journal of Systems and Software 165, 110,570 (2020)

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.087713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 32ec449b-6ade-47ce-a1d2-8a31baf9a7c8 · outbound

This paper cites TestART: Improving LLM-based Unit Testing via Co-evolution of Automated Generation and Repair Iteration.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts TestART: Improving LLM-based Unit Testing via Co-evolution of Automated Generation and Repair Iteration

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T15:12:04.739489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ec17fdb8-5ac3-4dc3-b0c8-5e96bea86e73 · outbound

This paper cites Big Data and Cognitive Computing6(3), 88 (2022).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Big Data and Cognitive Computing6(3), 88 (2022)

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:06.041110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 05405627-2f22-4b46-9152-5abc80e1d77b · outbound

This paper cites In: International Conference on Text, Speech, and Dialogue, pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: International Conference on Text, Speech, and Dialogue, pp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.996973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e6e441f9-7a24-4ec2-a6d6-f0b380dea487 · outbound

This paper cites arabic chatbots: A survey and future directions.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts arabic chatbots: A survey and future directions

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T15:12:04.760236Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcbd9445-4ae5-42cb-a31d-94e0fd9b307f · outbound

This paper cites In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pp

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T15:12:04.766341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41c62663-4a72-4ac2-aa8d-474cbbe37ba1 · outbound

This paper cites Journal of Computer Science (2016).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Journal of Computer Science (2016)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.908063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c119c0c1-7482-422c-bdcf-4518feaa3c3c · outbound

This paper cites ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)17(4), 1–28 (2018).

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.857302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aea8df15-a00d-4d24-be65-408f5a4ceddc · outbound

This paper cites Springer Nature (2022).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Springer Nature (2022)

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.813539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a783dcdb-604c-49be-83cb-0d722b88836c · outbound

This paper cites an unresolved cited work.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-11T15:12:05.778988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 81634702-dd07-4dfb-ae31-c04b1f691512 · outbound

This paper cites In: 2012 19th Working Conference on Reverse Engineering, pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: 2012 19th Working Conference on Reverse Engineering, pp

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.749316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 34172769-b816-4a19-bd26-70633c26a8d0 · outbound

This paper cites In: International Conference on Evaluation of Novel Software Approaches to Software Engineering, vol.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: International Conference on Evaluation of Novel Software Approaches to Software Engineering, vol

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.716566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 37f2f950-9424-43bc-b32d-0b2425a1399a · outbound

This paper cites In: 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: 2024 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), pp

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.689001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 42171505-cfbd-4372-a1e6-7edc153616cd · outbound

This paper cites In: 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: 2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), pp

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.660955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8146f849-9304-46a4-917e-33b91d25f161 · outbound

This paper cites O’Reilly Media, Inc.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts O’Reilly Media, Inc

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.633450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b2c2cbf4-0340-4f9c-90ac-eb41f4ec1c4f · outbound

This paper cites Natural Language Engineering9(4), 381–420 (2003).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Natural Language Engineering9(4), 381–420 (2003)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.600027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a4d2e3a4-9f3d-4369-a280-42d2d7a163c7 · outbound

This paper cites Ieee Access 12, 25,553–25,579 (2024).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Ieee Access 12, 25,553–25,579 (2024)

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.574100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d0dbaf4a-4ed1-49b3-aa26-d13d9abdb10a · outbound

This paper cites Applied Soft Computing132, 109,803 (2023).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Applied Soft Computing132, 109,803 (2023)

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.545629Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5b67a916-9a9c-4c58-9886-1d020bca5dca · outbound

This paper cites IEEE Access (2024).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts IEEE Access (2024)

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.509803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fa36a8f4-a5af-4ddb-b797-736e9356b34e · outbound

This paper cites Proceedings of the ACM on Software Engineering1(FSE), 951– 971 (2024).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Proceedings of the ACM on Software Engineering1(FSE), 951– 971 (2024)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.470490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3242c96c-a412-4712-953b-14f3f50b3eb0 · outbound

This paper cites IEEE Access (2024).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts IEEE Access (2024)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.442639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d2a5efca-c15f-4b10-8bd9-b4d9ec6b0ae4 · outbound

This paper cites IEEE Transactions on Soft- ware Engineering (2023).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts IEEE Transactions on Soft- ware Engineering (2023)

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.413446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2458db12-134e-4ad4-bc22-05175cf729c5 · outbound

This paper cites Software quality journal30(2), 455–481 (2022).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Software quality journal30(2), 455–481 (2022)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.365544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 27a52401-9ddb-48c1-915e-15ad07783240 · outbound

This paper cites In: International conference on intelligent systems design and applications, pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: International conference on intelligent systems design and applications, pp

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.333671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 709e7fb2-f011-4212-baf8-a0c38e104528 · outbound

This paper cites Attention Is All You Need.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Attention Is All You Need

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T15:12:04.963895Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e189962-5f43-4b89-9a6f-8d331c8ef1e7 · outbound

This paper cites In: International conference on artificial intelligence in education, pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: International conference on artificial intelligence in education, pp

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.285430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6fae5d14-8f29-440b-91b2-07f94633f2d2 · outbound

This paper cites Advances in Neural Information Processing Systems 36 (2024).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Advances in Neural Information Processing Systems 36 (2024)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.251559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fbdf0faa-ffc4-4647-96b4-62c993a73680 · outbound

This paper cites In: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings, pp.

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts In: Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings, pp

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.205130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1615f6c8-73d1-4b80-a71a-cdb843112366 · outbound

This paper cites Advances in Neural Information Processing Systems36, 58,478– 58,507 (2023).

RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts Advances in Neural Information Processing Systems36, 58,478– 58,507 (2023)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T15:12:05.163836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation fea86c3a-a851-45e9-b8e9-1bc70d8129f3 · inbound

A Multi-Layered Large Language Model Framework for Disease Prediction cites this paper.

A Multi-Layered Large Language Model Framework for Disease Prediction RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts

Reference 13

Resolution: metadata mismatch; local_arxiv, observed 2026-08-09T22:58:13.460089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 0136d469-3dc0-4b32-9af6-961f7fa492a1 · inbound

Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning cites this paper.

Retrieval Augmented Generation Based LLM Evaluation For Protocol State Machine Inference With Chain-of-Thought Reasoning RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T00:49:15.401352Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e700c880-545b-4529-bbcf-9b243d8218a6 · inbound

Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records cites this paper.

Scaling Arabic Medical Chatbots Using Synthetic Data: Enhancing Generative AI with Synthetic Patient Records RIRO: Reshaping Inputs, Refining Outputs Unlocking the Potential of Large Language Models in Data-Scarce Contexts

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-04T18:10:02.613966Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.