LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis
Synthesizing long-context supervised fine-tuning (SFT) data is a scalable way to enhance the long-context understanding of large language models (LLMs), yet existing approaches share three limitations: narrow task coverage, insufficient instruction difficulty, and a lack of faithfulness supervision. We propose \textbf{LongCrafter}, a structured synthesis framework that couples a hierarchical task taxonomy with an evidence-grounded pipeline. The taxonomy organizes long-context understanding into local/shallow and global/deep levels and yields 32 fine-grained task types that serve as a global ge
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- PossiblePossibly related (embedding) · 48%RimantasZ/contextspy →
- PossiblePossibly related (embedding) · 47%chrisliu298/awesome-llm-unlearning →
- LinkedLinked via arxiv author · 85%Chenhao Yuan →
“LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis”
- LinkedLinked via arxiv author · 85%Yinhao Xu →
“LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis”
- LinkedLinked via arxiv author · 85%Shuwen Xu →
“LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis”
- LinkedLinked via arxiv author · 85%Xizhi Yang →
“LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis”
- LinkedLinked via arxiv author · 85%Jiaxiang Liu →
“LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis”
- LinkedLinked via arxiv author · 85%Chenxi Zhou →
“LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis”