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.
From the blog
How to Train ChatGPT With Your Own Website Data
Training ChatGPT with your own data can provide the model with a better understanding of your unique context, allowing for more accurate and relevant responses.
Herman Schutte
Founder
How AI Chatbots Can Save You 100s Of Hours In Customer Support
Dive into the transformative power of AI chatbots in customer support. Learn how businesses can save significant time and enhance customer satisfaction, with a look at tools like SiteSpeakAI.
Herman Schutte
Founder