LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents | Paper Citation Record

Observation 6b4ec868-c1a4-48f3-9c31-6a5627857548 · outbound

This paper cites Prompt Design and Engineering: Introduction and Advanced Methods.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Prompt Design and Engineering: Introduction and Advanced Methods

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This paper cites Nature Reviews Physics5(5), 277–280 (2023).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Nature Reviews Physics5(5), 277–280 (2023)

Reference 2

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This paper cites Advances in neural information processing systems33, 1877–1901 (2020) 16 Klem et al.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Advances in neural information processing systems33, 1877–1901 (2020) 16 Klem et al

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This paper cites think like a lawyer.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents think like a lawyer

Reference 4

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This paper cites In: International Conference on Business Process Modeling, Development and Support.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: International Conference on Business Process Modeling, Development and Support

Reference 5

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This paper cites Amicus Curiae35, 28 (2001).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Amicus Curiae35, 28 (2001)

Reference 6

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This paper cites A Survey on Mixture of Experts in Large Language Models.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents A Survey on Mixture of Experts in Large Language Models

Reference 7

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This paper cites Metaverse Basic and Applied Research2, 33–33 (2023).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Metaverse Basic and Applied Research2, 33–33 (2023)

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:59.439848Z

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This paper cites IEEE Transactions on Knowledge and Data Engineering (2024).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents IEEE Transactions on Knowledge and Data Engineering (2024)

Reference 9

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This paper cites A Review of Multi-Modal Large Language and Vision Models.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents A Review of Multi-Modal Large Language and Vision Models

Reference 10

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This paper cites LEGAL-BERT: The Muppets straight out of Law School.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents LEGAL-BERT: The Muppets straight out of Law School

Reference 11

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This paper cites LexGLUE: A Benchmark Dataset for Legal Language Understanding in English.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents LexGLUE: A Benchmark Dataset for Legal Language Understanding in English

Reference 12

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This paper cites BooookScore: A systematic exploration of book-length summarization in the era of LLMs.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents BooookScore: A systematic exploration of book-length summarization in the era of LLMs

Reference 13

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This paper cites Sublanguage: Studies of language in restricted semantic domains pp.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Sublanguage: Studies of language in restricted semantic domains pp

Reference 14

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Observation e27da6c9-b8ec-41e6-9714-eae61d865583 · outbound

This paper cites Evaluating large language models in medical applications: a survey.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Evaluating large language models in medical applications: a survey

Reference 15

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This paper cites In: International Conference on Applications of Natural Language to Information Systems.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: International Conference on Applications of Natural Language to Information Systems

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:58.615138Z

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This paper cites LegaLMFiT: Efficient Short Legal Text Classification with LSTM Language Model Pre-Training.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents LegaLMFiT: Efficient Short Legal Text Classification with LSTM Language Model Pre-Training

Reference 17

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This paper cites The Cambridge Law Journal5(3), 366–370 (1935).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents The Cambridge Law Journal5(3), 366–370 (1935)

Reference 18

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Observation 48269ae0-3403-49fc-831c-b288ac004e90 · outbound

This paper cites In: The Thirty-eighth Annual Conference on Neural Information Processing Systems (2024).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: The Thirty-eighth Annual Conference on Neural Information Processing Systems (2024)

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:58.344195Z

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Observation 72afe5d3-8673-43ff-8636-e7d7c88445f2 · outbound

This paper cites SaulLM-7B: A pioneering Large Language Model for Law.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents SaulLM-7B: A pioneering Large Language Model for Law

Reference 20

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This paper cites Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture

Reference 21

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Observation 3f3da29d-f213-427b-9f7f-2b16ba96ecd5 · outbound

This paper cites In: Companion Proceedings of the 29th International Conference on Intelligent User Interfaces.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: Companion Proceedings of the 29th International Conference on Intelligent User Interfaces

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:58.116395Z

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Observation 31f2cf86-fc70-4e7f-aab6-1794523522c1 · outbound

This paper cites Active Prompting with Chain-of-Thought for Large Language Models.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Active Prompting with Chain-of-Thought for Large Language Models

Reference 23

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This paper cites Authorea Preprints (2023).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Authorea Preprints (2023)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:57.899020Z

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This paper cites In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD ’96).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD ’96)

Reference 25

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This paper cites Retrieval-Augmented Generation for Large Language Models: A Survey.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Retrieval-Augmented Generation for Large Language Models: A Survey

Reference 26

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This paper cites Annals of biomedical engineering51(12), 2629–2633 (2023).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Annals of biomedical engineering51(12), 2629–2633 (2023)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:57.412195Z

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This paper cites LegalBench: Prototyping a Collaborative Benchmark for Legal Reasoning.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents LegalBench: Prototyping a Collaborative Benchmark for Legal Reasoning

Reference 28

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This paper cites CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review

Reference 29

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This paper cites International Medical Education 2(3), 198–205 (2023).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents International Medical Education 2(3), 198–205 (2023)

Reference 30

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This paper cites In: European Conference on Information Retrieval.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: European Conference on Information Retrieval

Reference 31

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This paper cites Lawyer LLaMA Technical Report.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Lawyer LLaMA Technical Report

Reference 32

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This paper cites In: European semantic web conference.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: European semantic web conference

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:56.917896Z

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This paper cites HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution

Reference 34

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LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: JSAI International Symposium on Artificial Intelligence

Reference 35

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This paper cites Large Language Models in Law: A Survey.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Large Language Models in Law: A Survey

Reference 36

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LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Unresolved cited work

Reference 37

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This paper cites A Benchmark for Lease Contract Review.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents A Benchmark for Lease Contract Review

Reference 38

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This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 39

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This paper cites Advances in Neural Information Processing Systems36 (2024).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Advances in Neural Information Processing Systems36 (2024)

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:56.491533Z

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This paper cites Transactions of the Association for Computational Linguistics12, 157–173 (2024).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Transactions of the Association for Computational Linguistics12, 157–173 (2024)

Reference 41

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This paper cites The Journal of Academic Librarianship49(4), 102720 (2023) 18 Klem et al.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents The Journal of Academic Librarianship49(4), 102720 (2023) 18 Klem et al

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:56.357155Z

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Observation de57ca33-258f-4fe4-af31-e352ea34d4e5 · outbound

This paper cites Case law retrieval: problems, methods, challenges and evaluations in the last 20 years.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Case law retrieval: problems, methods, challenges and evaluations in the last 20 years

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:48.796849Z

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This paper cites In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:56.205639Z

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Observation 30bedc67-64bd-4110-af9a-24976badfdc7 · outbound

This paper cites ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents ILDC for CJPE: Indian Legal Documents Corpus for Court Judgment Prediction and Explanation

Reference 45

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Observation 46b201bf-6d50-468c-8800-71002e02f249 · outbound

This paper cites In: International Conference on Artificial Intelligence in Education.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: International Conference on Artificial Intelligence in Education

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:56.085636Z

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Observation f9723e54-1400-4474-98d3-be84c55d71b2 · outbound

This paper cites Generative Representational Instruction Tuning.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Generative Representational Instruction Tuning

Reference 47

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Observation a4d2765c-f856-44b2-9387-3e1757e188b3 · outbound

This paper cites Southeast Europe Journal of Soft Computing12(1), 13–41 (2023).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Southeast Europe Journal of Soft Computing12(1), 13–41 (2023)

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:55.929334Z

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This paper cites A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3

Reference 49

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T11:48:53.749347Z

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Observation 76782ba9-0a8b-4cd2-9099-b2c33d225410 · outbound

This paper cites MultiLegalPile: A 689GB Multilingual Legal Corpus.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents MultiLegalPile: A 689GB Multilingual Legal Corpus

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:49.845504Z

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LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:48:55.800906Z

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Observation f67eb5ec-37f0-4388-ad88-34cd0b9c55db · outbound

This paper cites Behaviour & Information Technology pp.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Behaviour & Information Technology pp

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:55.690514Z

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Observation 6d981b0c-fd87-4b19-8711-05a16f21c541 · outbound

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LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Unresolved cited work

Reference 53

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:48:55.550598Z

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Observation 4d38aef6-eaaa-4a99-90c1-2ba0e9c708fb · outbound

This paper cites YaRN: Efficient Context Window Extension of Large Language Models.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents YaRN: Efficient Context Window Extension of Large Language Models

Reference 54

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Source-reported events for the cited work

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Observation d4869cbe-b56a-43d4-91bf-b8622da288d2 · outbound

This paper cites In: Legal Knowledge and Information Systems, pp.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: Legal Knowledge and Information Systems, pp

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:55.405073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6ff94372-74e3-41d4-8b95-3e888c1dc2d8 · outbound

This paper cites A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:50.229826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35115b91-8aee-4b67-86ac-cdb85e1b7c86 · outbound

This paper cites TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents TELeR: A General Taxonomy of LLM Prompts for Benchmarking Complex Tasks

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:50.306575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b007cc2-a2c5-40b9-80a2-226684051b03 · outbound

This paper cites In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents In: Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:55.270994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 94d4b875-2323-4700-bb78-ccf584e3c10a · outbound

This paper cites an unresolved cited work.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Unresolved cited work

Reference 59

Resolution: unresolved; raw_fallback, observed 2026-08-05T11:48:55.124092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b2fa638f-0094-4f3b-b796-86b7a2b50bdd · outbound

This paper cites Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation

Reference 60

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T11:48:53.532902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6a418dd3-fb68-4643-b7b6-e09d1d194bc9 · outbound

This paper cites one country, two systems.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents one country, two systems

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:54.996485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 55355070-670c-4941-bda7-45bb22dc1a85 · outbound

This paper cites Neurocomputing568, 127063 (2024).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Neurocomputing568, 127063 (2024)

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:50.677378Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 137f49c3-7bf0-48a2-abef-67db946e87bd · outbound

This paper cites LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents LawLuo: A Multi-Agent Collaborative Framework for Multi-Round Chinese Legal Consultation

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:50.732611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa66006f-fd29-4963-ab19-ec825fa0b713 · outbound

This paper cites Nature medicine29(8), 1930–1940 (2023).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Nature medicine29(8), 1930–1940 (2023)

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:50.786987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71b3b313-5ba9-4acc-8525-a9b3eec1d214 · outbound

This paper cites Legal Prompt Engineering for Multilingual Legal Judgement Prediction.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Legal Prompt Engineering for Multilingual Legal Judgement Prediction

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:50.856145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f119e0be-6cf0-459f-9def-39e4b3c0a9c1 · outbound

This paper cites Meta-Radiology p.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Meta-Radiology p

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:54.854645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0cf1390a-193a-489b-acc7-a722bd7cd2b0 · outbound

This paper cites Prompt Engineering for Healthcare: Methodologies and Applications.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Prompt Engineering for Healthcare: Methodologies and Applications

Reference 67

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T11:48:53.301379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f8bcb1a1-1d6c-4285-af60-f38557f9abfc · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:51.052744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ecf19d4-1f54-4c1d-90a0-96f4c2dc277d · outbound

This paper cites Advances in neural information processing systems35, 24824–24837 (2022).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Advances in neural information processing systems35, 24824–24837 (2022)

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:51.113397Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 054db707-a38d-4f37-a002-f7e2553413b5 · outbound

This paper cites A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:51.181923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e25c5e2-6ba8-45cf-9f80-fe491413890a · outbound

This paper cites BloombergGPT: A Large Language Model for Finance.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents BloombergGPT: A Large Language Model for Finance

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:51.259128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73914885-9805-4acd-9866-017acec9815d · outbound

This paper cites CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents CAIL2019-SCM: A Dataset of Similar Case Matching in Legal Domain

Reference 72

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T11:48:53.046467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e998665d-b46e-42e7-a69c-f8042e60a49a · outbound

This paper cites British Journal of Educational Technology55(1), 90–112 (2024).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents British Journal of Educational Technology55(1), 90–112 (2024)

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:54.714683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3c48cc10-d1ae-42e6-9dee-ba5103593021 · outbound

This paper cites A Survey on Multimodal Large Language Models.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents A Survey on Multimodal Large Language Models

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:51.432571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d16341e6-f316-448d-a9c8-5ef4e6b6c858 · outbound

This paper cites Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:51.487133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0528b318-de0b-40af-ac69-a5464667b8cf · outbound

This paper cites Legal Prompting: Teaching a Language Model to Think Like a Lawyer.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Legal Prompting: Teaching a Language Model to Think Like a Lawyer

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:51.591055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5da4d409-f650-46a5-a006-ecd48dd841e1 · outbound

This paper cites MM-LLMs: Recent Advances in MultiModal Large Language Models.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents MM-LLMs: Recent Advances in MultiModal Large Language Models

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:51.663360Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d587dc58-e5e7-444f-abc7-51216360f881 · outbound

This paper cites CitaLaw: Enhancing LLM with Citations in Legal Domain.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents CitaLaw: Enhancing LLM with Citations in Legal Domain

Reference 78

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T11:48:52.795319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 91b830ba-8187-4faa-b9d4-a831eeb9dbda · outbound

This paper cites Transactions of the Association for Computational Linguistics 11, 1114–1131 (2023) 20 Klem et al.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Transactions of the Association for Computational Linguistics 11, 1114–1131 (2023) 20 Klem et al

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T11:48:54.581992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ef91febe-48de-4b92-9ef7-1d28933c4e3f · outbound

This paper cites Automatic Chain of Thought Prompting in Large Language Models.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Automatic Chain of Thought Prompting in Large Language Models

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:51.875784Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 098fd411-ea29-471a-8b2a-8fe5689ac38b · outbound

This paper cites arXiv preprint arXiv:2401.11641 (2024).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents arXiv preprint arXiv:2401.11641 (2024)

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:51.930619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0672fd7-6dda-4c4b-9728-60078314d14b · outbound

This paper cites Recommender Systems in the Era of Large Language Models (LLMs).

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents Recommender Systems in the Era of Large Language Models (LLMs)

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:51.987929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7799bdf-f01b-464d-895e-ed9e837ebc29 · outbound

This paper cites A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming.

LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-05T11:48:52.090201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Outbound references

Pith citing papers