PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs
Quick Answer
The PlanE framework enhances extractive-based LLMs by optimizing data decomposition, instruction tuning, and prompt inference, significantly reducing annotation costs.
Quick Take
The Data-Tuning-Inference (DTI) planner selects optimal base- combinations, improving efficiency across various datasets and models, as demonstrated by experimental results.
Key Points
- PlanE integrates data decomposition, instruction tuning, and prompt inference for LLM optimization.
- The DTI planner selects optimal base-LLM combinations for specific datasets.
- Experimental results validate PlanE's effectiveness across various datasets and models.
- The framework aims to reduce the high annotation costs associated with LLM tuning.
- Code for PlanE is publicly available for further research and development.
Paper Resources
Source Excerpt
Enhancing the task-specific capabilities of (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 tasks. To address the above issues, we propose a \textbf{Plan}ning framework for constructing \textbf{E}xtractive-based LLMs called \textbf{PlanE}, which includes data decomposition, instruction tuning, and prompt inferenc
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