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 sampling. However, these methods often produce easy negatives that models can quickly learn, failing to sufficiently challenge the model. To address this issue, a novel self-supervised hard negative sampling technique is proposed that leverages a large language model (LLM) to generate hard negatives from the same cluster duri
The increasing scale and complexity of retrieval systems in recommendation engines demand more sophisticated negative sampling techniques that move beyond simplistic 'easy negatives'. LLMs offer a powerful new tool for generating higher-quality training data.
This development enhances the performance of large-scale recommendation systems and search engines, directly impacting user experience, engagement, and the efficiency of digital platforms. Better retrieval models lead to more relevant content delivery and potentially higher revenue.
The reliance on basic in-batch and out-of-batch negative sampling methods will decrease, giving way to more intelligent, LLM-powered hard negative sampling strategies that produce more robust two-tower models.
- · Large language model developers
- · E-commerce platforms
- · Social media companies
- · Digital content providers
- · Platforms using outdated retrieval ranking approaches
- · Teams unable to integrate LLMs into their ML pipelines
Recommendation and search quality on major platforms improves, leading to better user satisfaction and engagement.
The demand for advanced LLM integration capabilities in data science teams will increase, driving skill development and new tooling.
More efficient content discovery could reshape consumption patterns and market shares across various digital industries.
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Read at arXiv cs.AI