@inproceedings{xu-etal-2026-gcig,
title = "{GCIG}: {G}raph{RAG}-based Cross-document Instruction Generation for Boosting {LLM} Reasoning",
author = "Xu, Xiaoliang and
Yuan, Huang and
Wang, Junmei and
Xu, Can",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.871/",
doi = "10.18653/v1/2026.findings-acl.871",
pages = "17593--17609",
ISBN = "979-8-89176-395-1",
abstract = "Automatic instruction generation offers a low-cost, high-efficiency pathway for fine-tuning large language models (LLMs). However, existing methods struggle in knowledge-intensive domains and complex reasoning tasks due to their dependence on high-quality seed data, limited coverage of single-document knowledge, and repetitive content. To overcome these limitations, this paper presents GCIG, a GraphRAG-based Cross-document Instruction Generation framework. We begin by constructing an enhanced knowledge graph to provide a structural representation of the raw corpus, followed by LLM-driven selection of reliable subgraph-text pairs based on factuality and logical complementarity. Subsequently, we adaptively generate diverse questions through task-aware prompts and context-sensitive retrieval. Finally, we employ Chain-of-Thought reasoning to anchor entity paths and integrate scattered evidence, thereby closing logical gaps and improving answer coherence. Experiments on knowledge-intensive and multi-hop question-answering tasks demonstrate that GCIG outperforms existing methods, producing instruction data with stronger logical consistency and broader knowledge coverage for effective LLM fine-tuning. The code and data are publicly available at https://github.com/WhitEiller/GCIG."
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<abstract>Automatic instruction generation offers a low-cost, high-efficiency pathway for fine-tuning large language models (LLMs). However, existing methods struggle in knowledge-intensive domains and complex reasoning tasks due to their dependence on high-quality seed data, limited coverage of single-document knowledge, and repetitive content. To overcome these limitations, this paper presents GCIG, a GraphRAG-based Cross-document Instruction Generation framework. We begin by constructing an enhanced knowledge graph to provide a structural representation of the raw corpus, followed by LLM-driven selection of reliable subgraph-text pairs based on factuality and logical complementarity. Subsequently, we adaptively generate diverse questions through task-aware prompts and context-sensitive retrieval. Finally, we employ Chain-of-Thought reasoning to anchor entity paths and integrate scattered evidence, thereby closing logical gaps and improving answer coherence. Experiments on knowledge-intensive and multi-hop question-answering tasks demonstrate that GCIG outperforms existing methods, producing instruction data with stronger logical consistency and broader knowledge coverage for effective LLM fine-tuning. The code and data are publicly available at https://github.com/WhitEiller/GCIG.</abstract>
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%0 Conference Proceedings
%T GCIG: GraphRAG-based Cross-document Instruction Generation for Boosting LLM Reasoning
%A Xu, Xiaoliang
%A Yuan, Huang
%A Wang, Junmei
%A Xu, Can
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F xu-etal-2026-gcig
%X Automatic instruction generation offers a low-cost, high-efficiency pathway for fine-tuning large language models (LLMs). However, existing methods struggle in knowledge-intensive domains and complex reasoning tasks due to their dependence on high-quality seed data, limited coverage of single-document knowledge, and repetitive content. To overcome these limitations, this paper presents GCIG, a GraphRAG-based Cross-document Instruction Generation framework. We begin by constructing an enhanced knowledge graph to provide a structural representation of the raw corpus, followed by LLM-driven selection of reliable subgraph-text pairs based on factuality and logical complementarity. Subsequently, we adaptively generate diverse questions through task-aware prompts and context-sensitive retrieval. Finally, we employ Chain-of-Thought reasoning to anchor entity paths and integrate scattered evidence, thereby closing logical gaps and improving answer coherence. Experiments on knowledge-intensive and multi-hop question-answering tasks demonstrate that GCIG outperforms existing methods, producing instruction data with stronger logical consistency and broader knowledge coverage for effective LLM fine-tuning. The code and data are publicly available at https://github.com/WhitEiller/GCIG.
%R 10.18653/v1/2026.findings-acl.871
%U https://aclanthology.org/2026.findings-acl.871/
%U https://doi.org/10.18653/v1/2026.findings-acl.871
%P 17593-17609
Markdown (Informal)
[GCIG: GraphRAG-based Cross-document Instruction Generation for Boosting LLM Reasoning](https://aclanthology.org/2026.findings-acl.871/) (Xu et al., Findings 2026)
ACL