Search Results - "Large Language Models in coding"

Programming for the Next Generation: Transitioning from Traditional Models to Large Language Models in Coding Education for Kids

Authors: Zainab Rafique Muhammad Wasim Alina Ali

Source: 2025 6th International Conference on Advancements in Computational Sciences (ICACS). :1-6

Large Language Models Improve Coding Accuracy and Reimbursement in a Neonatal Intensive Care Unit

Authors: Emma Holmes Caroline Massarelli Felix Richter et al.

Attacks and Defenses for Large Language Models on Coding Tasks

Authors: Chi Zhang Zifan Wang Ruoshi Zhao et al.

Source: Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering. :2268-2272

Should we still teach or learn coding? A postgraduate student perspective on the use of large language models for coding in ecology and evolution

Authors: Heather Campbell Thomas Bluck Ella Curry et al.

Source: Methods in Ecology and Evolution, Vol 15, Iss 10, Pp 1767-1770 (2024)

Subject Terms: coding, education, ChatGPT, Ecology, Evolution, QH359-425, large language models, artificial intelligence, teaching, QH540-549.5

Access URL: https://doaj.org/article/f550b7ecc0f0498d9e1dd93e49668ab9

CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences

Authors: Martin Weyssow Aton Kamanda Xin Zhou et al.

Source: ACM Transactions on Software Engineering and Methodology.

Subject Terms: Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, Computer Science - Machine Learning, Computer Science - Computation and Language, Computation and Language (cs.CL), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2403.09032

Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation

Authors: Leah von der Heyde Anna-Carolina Haensch Bernd Weiß et al.

Source: Survey Research Methods, Vol 19, Iss 4 (2025)

Subject Terms: survey research, open-ended responses, Large Language Models (LLMs), text classification, prompt engineering, Social sciences (General), H1-99

File Description: electronic resource

Relation: https://ojs.ub.uni-konstanz.de/srm/article/view/8568; https://doaj.org/toc/1864-3361

Access URL: https://doaj.org/article/e4d9cd0d59bb43149036dd37ed8ba392

Detecting code paraphrased by large language models using coding style features

Authors: Shinwoo Park Hyundong Jin Jeong-won Cha et al.

Source: Engineering Applications of Artificial Intelligence. 162:112454

Can Large Language Models Replace Coding Specialists? Evaluating GPT Performance in Medical Coding Tasks

Authors: Yeli Feng

The Three-Body Solution: Evaluating Reliability of Large Language Models for Coding Open-Ended Responses

Authors: Bill Altermatt

Source: Proceedings of the 2025 AERA Annual Meeting.

The Three-Body Solution: Evaluating Reliability of Large Language Models for Coding Open-Ended Responses

Authors: Rachel Barnett

Source: AERA 2025.

AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation

Authors: von der Heyde, Leah Haensch, Anna-Carolina Weiß, Bernd et al.

Subject Terms: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Artificial Intelligence, Computers and Society (cs.CY), Computation and Language, Computers and Society, Computation and Language (cs.CL)

Access URL: http://arxiv.org/abs/2506.14634

Conversational AI as a Coding Assistant: Understanding Programmers' Interactions with and Expectations from Large Language Models for Coding

Authors: Akhoroz, Mehmet Yildirim, Caglar

Subject Terms: FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computer Science - Human-Computer Interaction, Human-Computer Interaction (cs.HC)

Access URL: http://arxiv.org/abs/2503.16508

Harnessing large language models for coding, teaching and inclusion to empower research in ecology and evolution

Authors: Natalie Cooper Adam T. Clark Nicolas Lecomte et al.

Source: Methods in Ecology and Evolution, Vol 15, Iss 10, Pp 1757-1763 (2024)

Subject Terms: coding, ChatGPT, inclusion, Ecology, Evolution, 4. Education, QH359-425, large language models, artificial intelligence, teaching, QH540-549.5

Access URL: https://doaj.org/article/61334f2af6de4ede9ea87ed40b0165db
http://hdl.handle.net/10141/623163

Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Authors: Baizhou Huang Shuai Lu Xiaojun Wan et al.

Source: Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). :1429-1450

Harnessing large language models for coding, teaching and inclusion to empower research in ecology and evolution

Authors: Cooper, Natalie Clark, Adam T Lecomte, Nicolas et al.

URL: http://hdl.handle.net/10141/623163

An Application of Large Language Models to Coding Negotiation Transcripts

Authors: Friedman, Ray Cho, Jaewoo Brett, Jeanne et al.

Subject Terms: FOS: Computer and information sciences, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computation and Language (cs.CL)

Access URL: http://arxiv.org/abs/2407.21037

CodeUltraFeedback: An LLM-as-a-Judge Dataset for Aligning Large Language Models to Coding Preferences

Authors: Weyssow, Martin Kamanda, Aton Zhou, Xin et al.

Index Terms: Computer Science - Software Engineering, Computer Science - Computation and Language, Computer Science - Machine Learning, text

URL: http://arxiv.org/abs/2403.09032

Transfer Attacks and Defenses for Large Language Models on Coding Tasks

Authors: Zhang, Chi Wang, Zifan Mangal, Ravi et al.

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Cryptography and Security, Cryptography and Security (cs.CR), Machine Learning (cs.LG)

Access URL: http://arxiv.org/abs/2311.13445

Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency

Authors: Huang, Baizhou Lu, Shuai Chen, Weizhu et al.

Subject Terms: Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computation and Language (cs.CL)

Access URL: http://arxiv.org/abs/2309.17272

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