2026-07-24 04:00 UTCOriginal source2 min readUpdated: 2026-07-24 08:22 UTC

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

PlanE proposes a planning framework for building extractive LLMs via data decomposition, instruction tuning, and prompt inference, and introduces a DTI planner to automatically select the optimal base-LLM and combinations, demonstrating effectiveness and generalizability.

SourcearXiv AIAuthor: Jiacheng Wang, Weiyan Zhang, Guangya Yu

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Key points

PlanE addresses high annotation costs and lack of optimization for tailoring LLMs to specific tasks through data decomposition, instruction tuning, and prompt inference.
The DTI planner selects the best base-LLM and its data-tuning-inference combinations for given datasets, improving construction efficiency.
Experiments validate PlanE across different datasets with the same base-LLM and on the same dataset with different base-LLMs.
The DTI planner shows generalizability under different optimization objectives; code is publicly available.

Why it matters

This matters because planE addresses high annotation costs and lack of optimization for tailoring LLMs to specific tasks through data decomposition, instruction tuning, and prompt inference.

Technical impact

May affect model selection, inference cost, product capability, and evaluation benchmarks.

This panel is AI-generated and reviewed for accuracy.

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[Submitted on 22 May 2026]

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

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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.

Subjects:

Artificial Intelligence (cs.AI)

Cite as: arXiv:2607.20470 [cs.AI]

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

https://doi.org/10.48550/arXiv.2607.20470

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Weiyan Zhang [view email] [v1] Fri, 22 May 2026 13:37:33 UTC (1,197 KB)

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