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LLM-based clustering improves hard negative sampling for two-tower retrieval models

By PulseAugur Editorial · [2 sources] ·

A new self-supervised hard negative sampling technique has been developed for large-scale two-tower retrieval models, commonly used in recommendation systems. This method utilizes a large language model (LLM) to cluster and generate challenging negative samples in real-time during training. The approach aims to improve model performance by providing more informative negatives than traditional in-batch or out-of-batch methods. Experiments and deployment in a large-scale online system indicate that this technique surpasses current industry standards, helps mitigate feedback loops, and reduces popularity bias. AI

IMPACT Enhances recommendation system performance by improving training data quality and reducing bias.

RANK_REASON Academic paper detailing a new technique for AI model training. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM-based clustering improves hard negative sampling for two-tower retrieval models

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Academic paper detailing a new technique for AI model training. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ivan Ji (Zihao), Liuyi Hu (Zihao), Harrison (Zihao), Zhao (Xiangjun), Lei Huang (Xiangjun), Qunshu Zhang (Xiangjun), Max (Xiangjun), Fan, Aameek Singh ·

    Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval

    arXiv:2607.00448v1 Announce Type: cross Abstract: The two-tower model has been widely used for large-scale recommendation systems, particularly in the retrieval stage. Industry standards for training two-tower models typically involve in-batch and/or out-of-batch negative samplin…

  2. arXiv cs.AI TIER_1 English(EN) · Aameek Singh ·

    Real-Time Hard Negative Sampling via LLM-based Clustering for Large-Scale Two-Tower Retrieval

    The two-tower model has been widely used for large-scale recommendation systems, particularly in the retrieval stage. Industry standards for training two-tower models typically involve in-batch and/or out-of-batch negative sampling. However, these methods often produce easy negat…

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