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Perplexity describes a training method for contextual embedding models, which encode each chunk of a document while keeping the full document in view. The approach aims to make retrieval representations more aware of surrounding context than standard chunk-by-chunk embedding. Readers evaluating retrieval-augmented systems should look for benchmark results and details on compute cost and latency before judging how much it improves on existing methods.
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Surse care acoperăPerplexity, arXiv
Ce este aceastaLectura unui model asupra articolului și a acoperirii conexe, scrisă pentru aiqbench. Nu sunt cuvintele publicației.
We built a new way to train contextual embedding models, which encode each chunk of a document with the whole document in view
Perplexity1 min de cititArticolul și perspectiva sunt în engleză.
We built a new way to train contextual embedding models, which encode each chunk of a document with the whole document in view Perplexity
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