arXiv:2607.01601v1 Announce Type: new Abstract: Large scale document deduplication must preserve semantic equivalence while remaining efficient over massive corpora. We present SemHash LLM, a multi granularity framework that unifies semantic projection hashing, attention weighted MinHash, contrastive boundary learning, and selective LLM based adjudication. The method combines character, token, and document level signals through gated fusion, then applies a cascaded filtering pipeline for efficient candidate reduction. Semantic projection hashing learns compact binary codes in distilled LLM emb
The rapid growth of massive language models and digital content necessitates efficient methods for managing information duplication, which SemHash-LLM directly addresses.
Efficient document deduplication is critical for large-scale AI training data curation, preventing model bias, and optimizing computational resources for enterprise and public sector applications.
This framework offers a more robust and scalable approach to identifying semantic equivalence in vast datasets, improving the quality and efficiency of data processing workflows.
- · Big Tech AI labs
- · Cloud providers
- · Data management platforms
- · Large language model developers
- · Inefficient data processing methods
- · Organizations with poor data governance
Improved training data quality for large language models leading to more capable and less biased AI systems.
Reduced computational costs and shorter development cycles for new AI models due to better data curation.
Accelerated deployment of AI applications across various industries, as data preprocessing becomes more streamlined and effective.
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Read at arXiv cs.AI