Elamur Research⌕
☰
home / papers / 2026 / PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs

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

Paper · 2026 · Jiacheng Wang, Weiyan Zhang and Guangya Yu

Summary

Adapting an LLM to a particular information extraction (IE) task involves many interacting choices: how to prepare the training data, how to tune the model, and how to prompt it at inference time. The best combination changes with the dataset and with the base model, so trying every option is expensive. PlanE addresses this for extractive-based LLMs by defining a shared configuration space across these three stages:

  • Data: no decomposition, Pipeline-based, or Bidirectional splitting into atomic subtasks.
  • Tuning: SFT alone, or SFT followed by GRPO, DPO, or KTO.
  • Inference: direct, intersection, or union prompting.

A DTI planner, meta-learned from empirical runs, then predicts which Data-Tuning-Inference combination suits a given dataset or base-LLM. It can target F1 alone or a weighted mix of F1 and efficiency.

The framework was tested on relation extraction, event extraction, and aspect-based sentiment analysis with four 8–9B models. The planner beats a MetaGPT baseline and reaches Grid Search quality at much lower search cost. This matters for teams that must build task-specific extractors with limited annotation and compute budgets.

Key points

  • Datasets and models: CMeIE-V2 (RE), ACE05 (EE), and 14Lap (ABSA); base-LLMs Qwen3-8B, GLM4-9B, LLama3.1-8B, and InternLM3-8B.
  • Main result: single-objective F1 is 1.36% higher than MetaGPT. Results match Grid Search in both single- and multi-objective modes while saving about 554,833 seconds of search.
  • Multi-objective scoring: the score combines min-max-normalized F1 and training time with equal weights (α = β = 0.5).
  • Ablation: the best combination raised Qwen3-8B on ACE05 from 46.06 to 54.88 F1, and GLM4-9B reached 68.32 F1 on 14Lap. The effect of each factor varied across models and datasets with no consistent pattern.
  • Code: available at github.com/gugugu-469/PlanE.
  • Limitations: the planner is discrete, so it cannot be optimized with gradients, and it uses a simple scalar encoding of combinations.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🏢Ant GroupHangzhou · China42 works · 1,131 citations🎓East China University of Science and TechnologyShanghai · China10 works · 87 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksInformation extraction · 167 works

Cite

Cite as Jiacheng Wang, Weiyan Zhang and Guangya Yu. 2026. PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.1582

@inproceedings{wang2026plane,
  title = {{PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs}},
  author = {Jiacheng Wang, Weiyan Zhang and Guangya Yu},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1582},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1582}
}

References

33 references, 12 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Sadhana Kumaravel, Matthew Stallone, Rameswar Panda, Yara Rizk, G P Shrivatsa Bhargav, Maxwell Crouse, Chulaka Gunasekara, Shajith Ikbal, Sachindra Joshi, Hima Karanam, Vineet Kumar, Asim Munawar, Sumit Neelam, Dinesh Raghu, Udit Sharma, Adriana Meza S… find
  2. Reza Yazdani Aminabadi, Samyam Rajbhandari, Ammar Ahmad Awan, Cheng Li, Du Li, Elton Zheng, Olatunji Ruwase, Shaden Smith, Minjia Zhang, Jeff Rasley, and 1 others. 2022. Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale. In SC22: International Conference… find
  3. Menachem Brief, Oded Ovadia, Gil Shenderovitz, Noga BenYoash, Rachel Lemberg, and Eitam Sheetrit. 2024. Mixing it up: The cocktail effect of multi-task fine-tuning on LLM performance - a case study in finance. find
  4. Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, Xun Chen, Zehui Chen, Zhi Chen, Pei Chu, and 1 others. 2024. Internlm2 technical report. arXiv preprint arXiv:2403.17297.
  5. Dan A Calian, Gregory Farquhar, Iurii Kemaev, Luisa M Zintgraf, Matteo Hessel, Jeremy Shar, Junhyuk Oh, András György, Tom Schaul, Jeffrey Dean, and 1 others. 2025. Datarater: Meta-learned dataset curation. arXiv preprint arXiv:2505.17895. find
  6. Kaiyan Chang, Kun Wang, Nan Yang, Ying Wang, Dantong Jin, Wenlong Zhu, Zhirong Chen, Cangyuan Li, Hao Yan, Yunhao Zhou, and 1 others. 2024. Data is all you need: Finetuning llms for chip design via an automated design-data augmentation framework. In Proceedings of the 61st ACM/IEEE Design Automation… find
  7. Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, and Shafiq Joty. 2024. Data augmentation using LLMs: Data perspectives, learning paradigms and challenges. In Findings of the Association for Computational Linguistics: ACL 2024, pages… find
  8. Haonan Dong, Haoran Ye, Wenhao Zhu, Kehan Jiang, and Guojie Song. 2025. Meta-r1: Empowering large reasoning models with metacognition. arXiv preprint arXiv:2508.17291. — [arXiv:2508.17291](https://arxiv.org/abs/2508.17291) find
  9. Yipeng Du, Zihao Wang, Ahmad Farhan, Claudio Angione, Harry Yang, Fielding Johnston, James P. Buban, Yue Zhao, and Yuzhe Yang. 2025. Meta-learning for speeding up large model inference in decentralized environments. In Second Conference on Language Modeling. find
  10. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, and 1 others. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783.
  11. Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela. 2024. Kto: Model alignment as prospect theoretic optimization. arXiv preprint arXiv:2402.01306. find
  12. Team GLM, Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, Dan Zhang, Diego Rojas, Guanyu Feng, Hanlin Zhao, and 1 others. 2024. Chatglm: A family of large language models from glm-130b to glm-4 all tools. arXiv preprint arXiv:2406.12793.
  13. Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, E… — Training Compute-Optimal Large Language Models
  14. Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jürgen Schmidhuber. 2024. MetaGPT: Meta programming for a multi-agent collaborative framework. In The T…
  15. Yichuan Li, Kaize Ding, Jianling Wang, and Kyumin Lee. 2024a. Empowering large language models for textual data augmentation. In Findings of the Association for Computational Linguistics: ACL 2024, pages 12734–12751, Bangkok, Thailand. Association for Computational Linguistics. find
  16. Zhuowan Li, Cheng Li, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky. 2024b. Retrieval augmented generation or long-context LLMs? a comprehensive study and hybrid approach. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 881– 893,… find
  17. Chenwei Lou, Zewei Sun, Xinnian Liang, Meng Qu, Wei Shen, Wenqi Wang, Yuntao Li, Qingping Yang, and Shuangzhi Wu. 2025. Adacot: Pareto-optimal adaptive chain-of-thought triggering via reinforcement learning. arXiv preprint arXiv:2505.11896. — [arXiv:2505.11896](https://arxiv.org/abs/2505.11896) find
  18. Tanmay Parekh, Pradyot Prakash, Alexander Radovic, Akshay Shekher, and Denis Savenkov. 2025. Dynamic strategy planning for efficient question answering with large language models. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 6038–6059. find
  19. Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023. Direct preference optimization: Your language model is secretly a reward model. In Thirty-seventh Conference on Neural Information Processing Systems.
  20. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, and 1 others. 2024. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300.
  21. Weihang Su, Yichen Tang, Qingyao Ai, Zhijing Wu, and Yiqun Liu. 2024. DRAGIN: Dynamic retrieval augmented generation based on the real-time information needs of large language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers),…
  22. Qwen Team. 2025. Qwen3: Think deeper, act faster. find
  23. Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore. 1996. Reinforcement learning: A survey. Journal of artificial intelligence research, 4:237–285. find
  24. Changxin Tian, Kunlong Chen, Jia Liu, Ziqi Liu, Zhiqiang Zhang, and Jun Zhou. 2025. Towards greater leverage: Scaling laws for efficient mixture-of-experts language models. arXiv preprint arXiv:2507.17702. — [arXiv:2507.17702](https://arxiv.org/abs/2507.17702) find
  25. Krishnateja Killamsetty, Sivasubramanian Durga, Ganesh Ramakrishnan, Abir De, and Rishabh Iyer. 2021. Grad-match: Gradient matching based data subset selection for efficient deep model training. In International Conference on Machine Learning, pages 5464–5474. PMLR. find
  26. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, and 1 others. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288.
  27. Christopher Walker, Stephanie Strassel, Julie Medero, and Kazuaki Maeda. 2006. find
  28. Ace 2005 multilingual training corpus. Linguistic Data Consortium, Philadelphia, 57:45. find
  29. Keheng Wang, Feiyu Duan, Peiguang Li, Sirui Wang, and Xunliang Cai. 2025. LLMs know what they need: Leveraging a missing information guided framework to empower retrieval-augmented generation. In Proceedings of the 31st International Conference on Computational Linguistics, pages 2379–2400, Abu Dhab…
  30. Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric Xing, and Zhiting Hu. 2024. Promptagent: Strategic planning with language models enables expert-level prompt optimization. In The Twelfth International Conference on Learning Representations. find
  31. Lu Xu, Hao Li, Wei Lu, and Lidong Bing. 2020. Position-aware tagging for aspect sentiment triplet extraction. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2339–2349.
  32. Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, and 1 others. 2022. Cblue: A chinese biomedical language understanding evaluation benchmark. In ACL, pages 7888–7915.
  33. Weiyan Zhang, Jiacheng Wang, Chuang Chen, Wanpeng Lu, Wen Du, Haofen Wang, Jingping Liu, and Tong Ruan. 2024. A bidirectional extraction-then-evaluation framework for complex relation extraction. IEEE Transactions on Knowledge and Data Engineering. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/wang-2026-plane-meta-planning-data-tuning/