@inproceedings{xing-etal-2025-llmsr,
title = "{LLMSR}@{XLLM}25: A Language Model-Based Pipeline for Structured Reasoning Data Construction",
author = "Xing, Hongrui and
Liu, Xinzhang and
Jiang, Zhuo and
Yang, Zhihao and
Yao, Yitong and
Wang, Zihan and
Deng, Wenmin and
Wang, Chao and
Song, Shuangyong and
Yang, Wang and
He, Zhongjiang and
Li, Yongxiang",
editor = "Fei, Hao and
Tu, Kewei and
Zhang, Yuhui and
Hu, Xiang and
Han, Wenjuan and
Jia, Zixia and
Zheng, Zilong and
Cao, Yixin and
Zhang, Meishan and
Lu, Wei and
Siddharth, N. and
{\O}vrelid, Lilja and
Xue, Nianwen and
Zhang, Yue",
booktitle = "Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.xllm-1.31/",
doi = "10.18653/v1/2025.xllm-1.31",
pages = "342--350",
ISBN = "979-8-89176-286-2",
abstract = "In this paper, we present a novel pipeline for the XLLM Shared Task-III: Large Language Model for Structural Reasoning (LLM-SR). Our pipeline addresses key challenges in automatic process-reward training data construction, such as high manual annotation costs, limited accuracy of large models in structured data processing, and dependency on auxiliary information for validation. To overcome these limitations, we first decompose the construction process into extraction and validation phases. Leveraging model-generated annotations, we produce pseudo-labeled data and iteratively refine model performance. Second, by analyzing structured data patterns, we encode structural constraints into a rule-based module and fine-tune the model with Gradient Reward Policy Optimization (GRPO), significantly improving structured data extraction success rates. Finally, we train the model to generate critical responses that assess evidence-conclusion relationships, thus enhancing validation reliability. Experimental results demonstrate that our pipeline outperforms models with an order of magnitude more parameters and achieves the first position on the task."
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<abstract>In this paper, we present a novel pipeline for the XLLM Shared Task-III: Large Language Model for Structural Reasoning (LLM-SR). Our pipeline addresses key challenges in automatic process-reward training data construction, such as high manual annotation costs, limited accuracy of large models in structured data processing, and dependency on auxiliary information for validation. To overcome these limitations, we first decompose the construction process into extraction and validation phases. Leveraging model-generated annotations, we produce pseudo-labeled data and iteratively refine model performance. Second, by analyzing structured data patterns, we encode structural constraints into a rule-based module and fine-tune the model with Gradient Reward Policy Optimization (GRPO), significantly improving structured data extraction success rates. Finally, we train the model to generate critical responses that assess evidence-conclusion relationships, thus enhancing validation reliability. Experimental results demonstrate that our pipeline outperforms models with an order of magnitude more parameters and achieves the first position on the task.</abstract>
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%0 Conference Proceedings
%T LLMSR@XLLM25: A Language Model-Based Pipeline for Structured Reasoning Data Construction
%A Xing, Hongrui
%A Liu, Xinzhang
%A Jiang, Zhuo
%A Yang, Zhihao
%A Yao, Yitong
%A Wang, Zihan
%A Deng, Wenmin
%A Wang, Chao
%A Song, Shuangyong
%A Yang, Wang
%A He, Zhongjiang
%A Li, Yongxiang
%Y Fei, Hao
%Y Tu, Kewei
%Y Zhang, Yuhui
%Y Hu, Xiang
%Y Han, Wenjuan
%Y Jia, Zixia
%Y Zheng, Zilong
%Y Cao, Yixin
%Y Zhang, Meishan
%Y Lu, Wei
%Y Siddharth, N.
%Y Øvrelid, Lilja
%Y Xue, Nianwen
%Y Zhang, Yue
%S Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)
%D 2025
%8 August
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-286-2
%F xing-etal-2025-llmsr
%X In this paper, we present a novel pipeline for the XLLM Shared Task-III: Large Language Model for Structural Reasoning (LLM-SR). Our pipeline addresses key challenges in automatic process-reward training data construction, such as high manual annotation costs, limited accuracy of large models in structured data processing, and dependency on auxiliary information for validation. To overcome these limitations, we first decompose the construction process into extraction and validation phases. Leveraging model-generated annotations, we produce pseudo-labeled data and iteratively refine model performance. Second, by analyzing structured data patterns, we encode structural constraints into a rule-based module and fine-tune the model with Gradient Reward Policy Optimization (GRPO), significantly improving structured data extraction success rates. Finally, we train the model to generate critical responses that assess evidence-conclusion relationships, thus enhancing validation reliability. Experimental results demonstrate that our pipeline outperforms models with an order of magnitude more parameters and achieves the first position on the task.
%R 10.18653/v1/2025.xllm-1.31
%U https://aclanthology.org/2025.xllm-1.31/
%U https://doi.org/10.18653/v1/2025.xllm-1.31
%P 342-350
Markdown (Informal)
[LLMSR@XLLM25: A Language Model-Based Pipeline for Structured Reasoning Data Construction](https://aclanthology.org/2025.xllm-1.31/) (Xing et al., XLLM 2025)
ACL
- Hongrui Xing, Xinzhang Liu, Zhuo Jiang, Zhihao Yang, Yitong Yao, Zihan Wang, Wenmin Deng, Chao Wang, Shuangyong Song, Wang Yang, Zhongjiang He, and Yongxiang Li. 2025. LLMSR@XLLM25: A Language Model-Based Pipeline for Structured Reasoning Data Construction. In Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025), pages 342–350, Vienna, Austria. Association for Computational Linguistics.