Paper Citation Record · LEDGER

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks

As of 18 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2504.21074.

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

pith.paper-citation-record.v1
2504.21074 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:18:08.739066Z

measured 45 of 45 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 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

45 of 45 outbound references displayed

verified exact0
verified fuzzy29
unresolved16
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9a4aaac1-7c29-47d3-b3c8-09f676eb5a24 · outbound

This paper cites Information Systems 102, 101824 (2021).

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Information Systems 102, 101824 (2021)

Reference 1

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Observation e1e5a72c-1468-474f-b038-dc42af94c818 · outbound

This paper cites In: BPM, pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: BPM, pp

Reference 2

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Observation bd4aa957-ba30-4174-890b-41a76af1d8d6 · outbound

This paper cites Bridging Domain Knowledge and Process Discovery Using Large Language Models.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Bridging Domain Knowledge and Process Discovery Using Large Language Models

Reference 3

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Observation 42b427cc-6b28-487f-8ec1-7e87da6e7c3e · outbound

This paper cites In: International Conference on Business Process Management, pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: International Conference on Business Process Management, pp

Reference 4

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Observation 33e7f222-a037-4b66-873c-39058af2dca8 · outbound

This paper cites In: BPM, pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: BPM, pp

Reference 5

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Observation 9f55ad39-553d-4360-b765-e81a2a997473 · outbound

This paper cites Chit-Chat or Deep Talk: Prompt Engineering for Process Mining.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Chit-Chat or Deep Talk: Prompt Engineering for Process Mining

Reference 6

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Observation 79d56fdb-d13a-4777-94e6-35a5756e2c7b · outbound

This paper cites In: BPMDS, pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: BPMDS, pp

Reference 7

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Observation 41d84fb7-e505-4dfa-8134-b11500f27ed7 · outbound

This paper cites In: 2024 6th International Conference on Process Mining (ICPM), pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: 2024 6th International Conference on Process Mining (ICPM), pp

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:18:09.387664Z

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Observation 3042ce8e-43b2-4889-93b0-173d6ef59277 · outbound

This paper cites In: Process Mining Handbook, pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: Process Mining Handbook, pp

Reference 9

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Observation 4d209950-1e50-41e4-8cbb-9e114861f378 · outbound

This paper cites Electronic Notes in Theoretical Computer Science 121, 3–21 (2005).

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Electronic Notes in Theoretical Computer Science 121, 3–21 (2005)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:18:09.359012Z

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Observation c8d5649d-cc8e-407e-a9d3-7689733c0aa8 · outbound

This paper cites Information Systems 38(1), 33–44 (2013).

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Information Systems 38(1), 33–44 (2013)

Reference 11

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Observation 6a603730-e98a-4de4-bbcc-49ee5c6abf41 · outbound

This paper cites In: Process Mining Handbook, pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: Process Mining Handbook, pp

Reference 12

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This paper cites Artificial Intelligence Review 55(2), 801–827 (2022).

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Artificial Intelligence Review 55(2), 801–827 (2022)

Reference 13

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Unresolved cited work

Reference 14

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Observation 044928a8-14ae-4ac8-9128-782925d51754 · outbound

This paper cites In: BPM, pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: BPM, pp

Reference 15

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Observation 74e246f3-97ca-4644-97ed-f00685c5b2b7 · outbound

This paper cites Zenodo (2024).

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Zenodo (2024)

Reference 16

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Observation 67194360-e985-43c5-8a64-e0e4053e2b02 · outbound

This paper cites In: ICPM Workshops, pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: ICPM Workshops, pp

Reference 17

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Observation 2fd8de85-e86a-4b95-8f19-e7dd648f4abd · outbound

This paper cites In: Application and The- ory of Petri Nets and Concurrency: 34th International Conference, PETRI NETS 2013, Milan, Italy, June 24-28, 2013.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: Application and The- ory of Petri Nets and Concurrency: 34th International Conference, PETRI NETS 2013, Milan, Italy, June 24-28, 2013

Reference 18

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Observation 57d2602e-c680-44ed-86b7-f228e156213a · outbound

This paper cites Advances in neural information processing systems 30 (2017).

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Advances in neural information processing systems 30 (2017)

Reference 19

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This paper cites In: NAACL, pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: NAACL, pp

Reference 20

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This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 21

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks : Language models are few-shot learners

Reference 22

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks LLaMA: Open and Efficient Foundation Language Models

Reference 23

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks : Super- naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks

Reference 24

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This paper cites arXiv:2308.10792 (2023).

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks arXiv:2308.10792 (2023)

Reference 25

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This paper cites A Survey on In-context Learning.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks A Survey on In-context Learning

Reference 26

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: International Conference on Learning Representations (2021)

Reference 27

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks 56, (2018)

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This paper cites In: Interna- tional Conference on Learning Representations (2018).

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: Interna- tional Conference on Learning Representations (2018)

Reference 29

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: International Conference on Enterprise Design, Operations, and Computing, pp

Reference 30

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future Challenges

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: ICPM, pp

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This paper cites In: BPM, pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: BPM, pp

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Process Modeling With Large Language Models

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: International Conference on Business Process Management, pp

Reference 36

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This paper cites In: International Conference on Business Process Management, pp.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: International Conference on Business Process Management, pp

Reference 37

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: International Conference on Cooperative Information Systems, pp

Reference 38

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: International Conference on Process Mining, pp

Reference 39

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks In: BPMDS, pp

Reference 40

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This paper cites PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks.

On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks

Reference 41

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks LLM Evaluators Recognize and Favor Their Own Generations

Reference 42

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Towards a Benchmark for Large Language Models for Business Process Management Tasks

Reference 43

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks Evaluating Large Language Models on Business Process Modeling: Framework, Benchmark, and Self-Improvement Analysis

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On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks KI-K¨ unstliche Intelligenz, 1–15 (2024) 31

Reference 45

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Pith citing papers

No inbound Pith citation observations are available.