PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs

Researchers propose PlanE, a planning framework for extractive-based large language models (LLMs) that includes data decomposition, instruction tuning, and prompt inference, along with a Data-Tuning-Inference (DTI) planner to select optimal base-LLM and DTI combinations. Experimental results show effectiveness across different datasets and base-LLMs, with codes available on GitHub.

arXiv:2607.20470v1 Announce Type: new Abstract: 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 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 inference. Additionally, we introduce a Data-Tuning-Inference DTI planner, aimed at selecting the optimal base-LLM and its DTI combinations for specific datasets to improve construction efficiency. The experimental results demonstrate the effectiveness of our PlanE from two views: 1 across different datasets using the same base-LLM, and 2 on the same dataset using different base-LLMs. Furthermore, we validate the generalizability of the proposed DTI planner under different optimization objectives. The codes are publicly available at https://github.com/gugugu-469/PlanE.