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.

Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL). However, due to the feature divergence of an attribute (resp. object) when combined with different objects (resp. attributes), it is challenging to learn disentangled primitive features that are general across different compositions. To this end, we propose the solution of cross-composition feature disentanglement, which takes multiple primitive-sharing compositions as inputs and constrains the disentangled primitive features to be general across these compositions. More specifically, we leverage a compositional graph to define the overall primitive-sharing relationships between compositions, and build a task-specific architecture upon the recently successful large pre-trained vision-language model (VLM) CLIP, with dual cross-composition disentangling adapters (called L-Adapter and V-Adapter) inserted into CLIP’s frozen text and image encoders, respectively. Evaluation on three popular CZSL benchmarks shows that our proposed solution significantly improves the performance of CZSL, and its components have been verified by solid ablation studies. Our code and data are available at: https://github.com/zhurunkai/DCDA.