What is Negative Sampling in Retrieval?
A training technique where irrelevant data points are sampled to improve the performance of retrieval models.
More about Negative Sampling in Retrieval:
Negative Sampling in Retrieval is a method used to train retrieval models by introducing irrelevant examples (negatives) during the learning process. This helps the model differentiate between relevant and irrelevant data points, improving its ability to rank results accurately.
Negative sampling is a critical component in training tasks like dense retrieval, semantic search, and retrieval fusion, where distinguishing relevance is essential.
Frequently Asked Questions
Why is negative sampling important in retrieval training?
It helps models learn to identify irrelevant results, improving the precision of retrieval systems.
How are negative samples selected?
They can be selected randomly or through techniques like hard negative mining, which focuses on challenging examples.
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