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🤖 We train LLMs — but how do LLMs reshape AI research itself?
Sharing our new survey, AI4AIR: A Comprehensive Survey on Large Language Models for AI Research 📚
Rather than treating LLMs as outside helpers for writing or literature review, we map how they enter the ML pipeline itself — through a 2D taxonomy (research domain × pipeline stage) and 5 recurring roles:
🏷️ Annotator · 🧪 Synthesizer · ⚙️ Optimizer · 📊 Evaluator · 🎛️ Orchestrator
Our key takeaway: how deeply an LLM can empower a stage is bounded by its validation cost — which is exactly why data annotation & evaluation are already mature, while model design & full-loop orchestration remain hard.
📄 Paper (PDF): https://github.com/ICT-FinD-Lab/Awesome-LLMs-for-AI-Research/blob/master/PDF/AI4AIR_Survey_v260601.pdf
🌐 Project: https://ict-find-lab.github.io/Awesome-LLMs-for-AI-Research
⭐ GitHub: https://github.com/ICT-FinD-Lab/Awesome-LLMs-for-AI-Research
Stars & feedback warmly welcome 🙌
Sharing our new survey, AI4AIR: A Comprehensive Survey on Large Language Models for AI Research 📚
Rather than treating LLMs as outside helpers for writing or literature review, we map how they enter the ML pipeline itself — through a 2D taxonomy (research domain × pipeline stage) and 5 recurring roles:
🏷️ Annotator · 🧪 Synthesizer · ⚙️ Optimizer · 📊 Evaluator · 🎛️ Orchestrator
Our key takeaway: how deeply an LLM can empower a stage is bounded by its validation cost — which is exactly why data annotation & evaluation are already mature, while model design & full-loop orchestration remain hard.
📄 Paper (PDF): https://github.com/ICT-FinD-Lab/Awesome-LLMs-for-AI-Research/blob/master/PDF/AI4AIR_Survey_v260601.pdf
🌐 Project: https://ict-find-lab.github.io/Awesome-LLMs-for-AI-Research
⭐ GitHub: https://github.com/ICT-FinD-Lab/Awesome-LLMs-for-AI-Research
Stars & feedback warmly welcome 🙌