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

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 this https URL.

Artificial Intelligence (cs.AI)

arXiv:2607.20470 [cs.AI]

(or arXiv:2607.20470v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.20470 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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