AI Research
PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs
Enhancing the task-specific capabilities of Large Language Models (LLMs) primarily requires substantial instruction-tuning datasets. However, the sheer volume of such data imposes a considerable annotation cost, and a lack of optimization methods for tailoring LLMs to specific ta...
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ArXiv CS.AI
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ArXiv CS.AI
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PlanE addresses the high cost of LLM instruction tuning by introducing a planning framework for data, tuning, and inference. This approach promises to improve the efficiency and effectiveness of tailoring LLMs for specific extractive tasks.
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