arXiv:2605.31238v1 Announce Type: cross Abstract: Endowing large language models with compositional reasoning over specialized documents requires multi-hop training data at scale, where such data rarely exists outside of curated benchmarks built on structured sources. To construct it directly from plain, unannotated text, existing methods ask a single teacher model to jointly discover an evidence path through a document and verbalize it as a question-answer pair. However, these methods degrade sharply when documents are structured around repetitive templates and densely cross-referencing claus
The paper directly addresses the current limitations in scaling multi-hop training data, a critical bottleneck for advancing large language models' reasoning capabilities.
Improving multi-hop reasoning is essential for AI systems to process complex, interlinked information, enhancing their utility in specialized and real-world applications.
This method aims to enable more robust and scalable generation of training data for complex reasoning, moving beyond curated benchmarks to unannotated text.
- · AI research labs
- · Large language model developers
- · Data scientists
- · Specialized AI applications
- · Companies relying on manual data curation for complex reasoning tasks
Increased availability of high-quality, multi-hop training data for large language models.
Improved compositional reasoning capabilities in LLMs, making them more effective in tasks requiring deep understanding of documents.
Acceleration of AI development in fields requiring complex information synthesis, such as scientific discovery and legal analysis.
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Read at arXiv cs.LG