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Beyond chatbots: The wide world of embeddings
Embedding models are set to revolutionize AI in 2024, providing advanced data representation and retrieval capabilities for cutting-edge enterprise applications and large language model enhancements.
This simple mechanism plays a great role in customizing LLMs to respond based on proprietary documents or information that was not included in their training data. For example, embeddings can help companies categorize millions of customer feedback messages or social media posts to detect trends, common themes, and sentiment changes. “Currently, enterprises would need very strong in-house ML teams to make embedding finetuning effective, so it’s usually better to use out-of-the-box options, in contrast to other facets of LLM use cases,” Reimers said.
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