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SemHash-LLM Boosts Document Deduplication with Multi-Granularity Hashing

Xinyi Fang, Kejian Tong, Jiabei Liu, Tao Ning, Yuhang He· July 3, 2026 View original

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Key takeaways

  • SemHash-LLM offers a novel, efficient approach to large-scale document deduplication.
  • It combines multiple hashing techniques and LLM-based verification for high accuracy.
  • The framework is robust against various forms of text duplication and noise.
  • It significantly reduces the computational cost of neural verification.

Who benefits

Data ManagementContent PlatformsLegalTechResearch & AcademiaAI/ML Development

Summary

Researchers introduce SemHash-LLM, a framework for large-scale document deduplication that combines semantic projection hashing, attention-weighted MinHash, and LLM-based adjudication. It efficiently preserves semantic equivalence across massive datasets by integrating character, token, and document-level signals.

Large-scale document deduplication is crucial for managing vast text corpora, but it requires methods that are both semantically accurate and computationally efficient. A new framework, SemHash-LLM, addresses this challenge by employing a multi-granularity approach. It integrates several techniques, including semantic projection hashing, which learns compact binary codes from distilled LLM embeddings, and attention-weighted MinHash, designed to prioritize informative content while suppressing boilerplate. The system also incorporates contrastive boundary learning and selective LLM-based adjudication. By combining signals from character, token, and document levels through a gated fusion mechanism, SemHash-LLM applies a cascaded filtering pipeline to reduce candidate sets efficiently. This robust method is designed to handle various complexities like template pollution, short text perturbations, and viral fragments, significantly improving duplicate detection quality with minimal neural verification costs.

Why it matters

Professionals dealing with vast amounts of text data can leverage this framework to improve data quality, reduce storage costs, and enhance the efficiency of information retrieval and model training by eliminating redundant documents.

How to implement this in your domain

  1. 1Evaluate existing deduplication pipelines for efficiency and semantic accuracy.
  2. 2Integrate semantic hashing techniques into data preprocessing workflows for large text datasets.
  3. 3Experiment with multi-granularity signal fusion to optimize deduplication for specific content types.
  4. 4Utilize LLM-based adjudication for high-precision verification of potential duplicates.
  5. 5Monitor the impact on data storage, processing time, and downstream model performance.

Original post by Xinyi Fang, Kejian Tong, Jiabei Liu, Tao Ning, Yuhang He

"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…"

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Originally posted by Xinyi Fang, Kejian Tong, Jiabei Liu, Tao Ning, Yuhang He on X · view source

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