ML Research Hub
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Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.

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Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective

📝 Summary:
This study investigated software developers' perspectives on Large Language Models, identifying benefits like improved workflow and entrepreneurship, alongside risks to personal well-being and reputation. It highlights key trade-offs and best practices for adopting LLMs in software development.

🔹 Publication Date: Published on Nov 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06428
• PDF: https://arxiv.org/pdf/2511.06428

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For more data science resources:
https://t.iss.one/DataScienceT

#LLMs #SoftwareDevelopment #AIinDevelopment #DeveloperExperience #TechResearch
PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides

📝 Summary:
PPTAgent improves presentation generation with a two-stage approach that analyzes reference presentations to ensure structural and content consistency. It outperforms traditional methods across content, design, and coherence.

🔹 Publication Date: Published on Jan 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2501.03936
• PDF: https://arxiv.org/pdf/2501.03936
• Github: https://github.com/icip-cas/PPTAgent

Datasets citing this paper:
https://huggingface.co/datasets/Forceless/Zenodo10K

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For more data science resources:
https://t.iss.one/DataScienceT

#AIPresentations #GenerativeAI #MachineLearning #NLP #TechResearch