2023/11/28 by Marina Yue Zhang, Marina Zhang, Owen Vallis +9 · 1 voice · 1 citation
Computer Science · Engineering · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Geophysical Methods and Applications #Handwritten Text Recognition Techniques #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2311.17264
openalex publication_date 2023/11/28 · arxiv published 2023/11/28 · arxiv updated 2023/11/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper introduces RETSim (Resilient and Efficient Text Similarity), a lightweight, multilingual deep learning model trained to produce robust metric embeddings for near-duplicate text retrieval, clustering, and dataset deduplication tasks. We demonstrate that RETSim is significantly more robust and accurate than MinHash and neural text embeddings, achieving new state-of-the-art performance on dataset deduplication, adversarial text retrieval benchmarks, and spam clustering tasks. We also introduce the W4NT3D benchmark (Wiki-40B 4dversarial Near-T3xt Dataset) for evaluating multilingual, near-duplicate text retrieval capabilities under adversarial settings. RETSim and the W4NT3D benchmark are open-sourced under the MIT License at https://github.com/google/unisim.