Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations | Paper Citation Record

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: Text Analysis for the Social Sciences, pp

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: Proceedings of Second Interna- tional Conference on Computing, Communications, and Cyber-Security: IC4S 2020, pp

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Physica Medica 83, 9–24 (2021)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Journal of the American Medical Informatics Association 27(3), 491–497 (2020)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: Product-Focused Software Process Improvement: 10th International Conference, PROFES 2009, Oulu, Finland, June 15-17,

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This paper cites ACM SIGSOFT Software Engineering Notes 35(3), 8–13 (2010).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations ACM SIGSOFT Software Engineering Notes 35(3), 8–13 (2010)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Computer 21(5), 61–72 (1988)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Agile Software Development Methods: Review and Analysis

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Auerbach Publications, ??? (2022)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2010 International Conference on Advances in Recent Technologies in Communication and Computing, pp

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Journal of King Saud University-Computer and Information Sciences 35(8), 101665 (2023)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2020 IEEE 28th International Requirements Engineering Conference (RE), pp

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2017 IEEE 25th International Requirements Engineering Conference (RE), pp

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Design Engineering, 2662–2678 (2021)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations symmetry 12(10), 1601 (2020)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), pp

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Information Technology and Control 48(3), 432–445 (2019)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2017 5th International Conference in Soft- ware Engineering Research and Innovation (CONISOFT), pp

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Springer, ??? (2023)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Proceed- ings of the IEEE 109(5), 612–634 (2021)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations International Journal of Approximate Reasoning 103, 1–10 (2018) Springer Nature 2021 LATEX template Generative Language Models 27

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations BMC medical informatics and decision making 20, 1–7 (2020)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Neurocomputing 177, 257–265 (2016)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2018 IEEE International Conference of Intelligent Robotic and Control Engineering (IRCE), pp

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Innovative Data Communication Tech- nologies and Application: Proceedings of ICIDCA 2020, 267–281 (2021)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Journal of big data 2(1), 1–21 (2015)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Artificial intelligence in medicine 117, 102083 (2021)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations IEEE access 7, 53040–53065 (2019)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations APSIPA transactions on signal and information processing 8, 19 (2019)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Southeast Europe Journal of Soft Computing 12(1), 13–41 (2023)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Finance Research Letters 53, 103662 (2023)

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2024 IEEE/ACM 21st International Conference on Mining Software Repositories (MSR), pp

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This paper cites In: 2023 International Conference on Computational Science and Springer Nature 2021 LATEX template 30 Generative Language Models Computational Intelligence (CSCI), pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2023 International Conference on Computational Science and Springer Nature 2021 LATEX template 30 Generative Language Models Computational Intelligence (CSCI), pp

Reference 58

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This paper cites 245–255 (2024).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations 245–255 (2024)

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.852312Z

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: International Conference on Analysis of Images, Social Networks and Texts, pp

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.856767Z

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Observation b3c78e4b-e575-44bf-bf78-f30fae6562f0 · outbound

This paper cites In: 2020 IEEE 28th International Requirements Engineering Conference (RE), pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2020 IEEE 28th International Requirements Engineering Conference (RE), pp

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.861384Z

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Observation d284cf92-68b9-4d8a-a53e-3dba39cb4625 · outbound

This paper cites Entropy 22(9), 1057 (2020).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Entropy 22(9), 1057 (2020)

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.865970Z

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: Journal of Physics: Conference Series, vol

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.870500Z

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This paper cites Journal of Software: Evolution and Process, 2430 (2022).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Journal of Software: Evolution and Process, 2430 (2022)

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.874992Z

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This paper cites Deep Learning Methods for Software Requirement Classification: A Performance Study on the PURE dataset.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Deep Learning Methods for Software Requirement Classification: A Performance Study on the PURE dataset

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.879389Z

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Observation bca1892c-88f8-4de8-bd38-bdcb4050bc67 · outbound

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Entropy 23(10), 1264 (2021)

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.884423Z

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Observation daffd54a-217a-46ee-8445-4c0368e2b732 · outbound

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Bioinformatics 37(15), 2112–2120 (2021)

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.466157Z

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Observation 1779ce12-38b2-46f7-bafb-1500382699da · outbound

This paper cites BERT_SE: A Pre-trained Language Representation Model for Software Engineering.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations BERT_SE: A Pre-trained Language Representation Model for Software Engineering

Reference 68

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T04:51:56.591955Z

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This paper cites In: 2020 IEEE Interna- tional Conference on Smart Cloud (SmartCloud), pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2020 IEEE Interna- tional Conference on Smart Cloud (SmartCloud), pp

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.452637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9f9a2b4c-3bfb-45ca-bb65-628c7f910251 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.902584Z

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Observation f280b006-887c-4ceb-9985-a34549250feb · outbound

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

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.907303Z

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Observation 7c2d4d89-9582-4921-8d9b-7eacaa216727 · outbound

This paper cites Advances in neural information processing systems 32 (2019).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Advances in neural information processing systems 32 (2019)

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.438695Z

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Observation 5b5241a3-f1c7-4ccf-9de3-b93d3bf70a32 · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.916295Z

Source-reported events for the cited work

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Observation e5de8105-2aa9-485c-8e14-aee6b46f80f8 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T04:51:55.921022Z

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Unavailable: canonical work link unavailable.

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This paper cites IEEE Access 10, 30080–30090 (2022).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations IEEE Access 10, 30080–30090 (2022)

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.424662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ff0f1831-3fc4-4c2c-a123-ba8954dc1b67 · outbound

This paper cites International Journal of Computer Science and Network Security 8(2), 339–344 (2008).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations International Journal of Computer Science and Network Security 8(2), 339–344 (2008)

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.410194Z

Source-reported events for the cited work

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Observation f102a8b5-3b7f-4be6-83d9-c2480677bf87 · outbound

This paper cites Journal of Systems and Software 165, 110572 (2020).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Journal of Systems and Software 165, 110572 (2020)

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.394973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f58f15dc-14a1-401b-a6d8-b9c09689e93b · outbound

This paper cites Technical report, Department of Computer Science, Virginia Polytechnic Institute & State.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Technical report, Department of Computer Science, Virginia Polytechnic Institute & State

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.380735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c298d102-57fe-4c2e-a12f-500245ef0bef · outbound

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Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Unresolved cited work

Reference 79

Resolution: unresolved; raw_fallback, observed 2026-08-12T04:51:57.365956Z

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This paper cites Code and Named Entity Recognition in StackOverflow.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Code and Named Entity Recognition in StackOverflow

Reference 80

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T04:51:56.509653Z

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Observation 37964458-d348-44c3-afe8-b79a1b5ceef8 · outbound

This paper cites In: 2019 IEEE International Conference on Smart Internet of Things (SmartIoT), pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2019 IEEE International Conference on Smart Internet of Things (SmartIoT), pp

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.350795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2db67807-0f6b-478a-a77f-2c8a5c2b4c6a · outbound

This paper cites In: 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW), pp

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.337256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f28843b9-4d35-4895-9b53-29fbf9bc0693 · outbound

This paper cites In: Proceedings of the 26th Conference on Program Comprehension, pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: Proceedings of the 26th Conference on Program Comprehension, pp

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.323793Z

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Observation 2ae27a0a-b614-4a11-9e8c-872597033dc1 · outbound

This paper cites In: 2022 2nd International Conference on Intelligent Technologies (CONIT), pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2022 2nd International Conference on Intelligent Technologies (CONIT), pp

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.309832Z

Source-reported events for the cited work

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Observation 967a3f93-c4d3-4f32-b8d3-5cbfdf796894 · outbound

This paper cites International Journal of Advanced Computer Science and Applications (IJACSA) (2021).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations International Journal of Advanced Computer Science and Applications (IJACSA) (2021)

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.294338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 70f0303e-3f10-41a0-be86-f74a4ee5e665 · outbound

This paper cites In: 2022 2nd Asian Con- ference on Innovation in Technology (ASIANCON), pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2022 2nd Asian Con- ference on Innovation in Technology (ASIANCON), pp

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.279569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6a46ff53-29ac-4254-a142-ce3ca82adc53 · outbound

This paper cites In: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering, pp

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.264402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 185b155f-89a6-45c4-9406-0fd98079d34a · outbound

This paper cites In: 2022 IEEE 30th International Requirements Engineering Conference (RE), pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2022 IEEE 30th International Requirements Engineering Conference (RE), pp

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.250149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f01ce139-4716-4a36-aa59-a73722efe236 · outbound

This paper cites In: Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering, pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: Proceedings of the 31st Annual International Conference on Computer Science and Software Engineering, pp

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.236393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 04f2d700-6f3b-40cb-8e75-61d12d65c90a · outbound

This paper cites In: Canadian Conference on AI, vol.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: Canadian Conference on AI, vol

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.222791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation df759565-6f0f-4c21-8991-6abaa88be346 · outbound

This paper cites In: Proceedings of the 3rd ACM SIGSOFT Interna- tional Workshop on Machine Learning Techniques for Software Quality Evaluation, pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: Proceedings of the 3rd ACM SIGSOFT Interna- tional Workshop on Machine Learning Techniques for Software Quality Evaluation, pp

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.208742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation dde893f6-6345-4835-be9e-7e42ba1c8ad7 · outbound

This paper cites In: 2019 IEEE 43rd Annual Computer Software and Applica- tions Conference (COMPSAC), vol.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2019 IEEE 43rd Annual Computer Software and Applica- tions Conference (COMPSAC), vol

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.194680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1d00f4a8-23f0-47d6-af17-ed7be12dc009 · outbound

This paper cites Advances in Science and Technology.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Advances in Science and Technology

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.180671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6fde4b73-a9a7-4ecf-bf91-643413f8cce2 · outbound

This paper cites In: 2019 IEEE 27th International Requirements Engineering Conference (RE), pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: 2019 IEEE 27th International Requirements Engineering Conference (RE), pp

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.166855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 694fbf96-577e-4cd7-9f9d-27ed25f92587 · outbound

This paper cites ACM Computing Surveys 56(2), 1–40 (2023).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations ACM Computing Surveys 56(2), 1–40 (2023)

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.152396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b31aefb5-bf4b-4ca8-83b8-61148cec0628 · outbound

This paper cites ACM Transactions on Computing for Healthcare (HEALTH) 3(1), 1–23 (2021).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations ACM Transactions on Computing for Healthcare (HEALTH) 3(1), 1–23 (2021)

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.137787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0dd5e2eb-658c-480b-b414-0f1227b1b8a5 · outbound

This paper cites In: AIAA SCITECH 2023 Forum, p.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: AIAA SCITECH 2023 Forum, p

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.122612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b479e142-d76e-445e-8b81-b2cec75cbe44 · outbound

This paper cites Journal of Information Processing 31, 143–153 (2023).

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Journal of Information Processing 31, 143–153 (2023)

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.108898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 68acc0fc-cae3-4092-963b-d49d48b0ae9b · outbound

This paper cites In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T04:51:57.094408Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3b0b6e8a-6ce5-40c1-ab69-191edc4dcb79 · outbound

This paper cites Transfer learning for conflict and duplicate detection in software requirement pairs.

Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations Transfer learning for conflict and duplicate detection in software requirement pairs

Reference 100

Resolution: metadata mismatch; local_arxiv, observed 2026-08-12T04:51:56.487402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

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Outbound references

Pith citing papers