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  • Input Embedding Formula in BERT-like Models

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In a language model that uses separate vectors for token identity, position, and sentence membership, the final input vector for a token is created by concatenating these three component vectors end-to-end.

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Updated 2025-10-04

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Ch.1 Pre-training - Foundations of Large Language Models

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  • A researcher is debugging a language model where the input representation for each token is created by summing three distinct vectors: one for the token's identity, one for its position in the sequence, and one for the sentence segment it belongs to. The researcher observes that the model treats the sentences 'The scientist observed the star' and 'The star observed the scientist' as having identical meanings. Which of the three component vectors is most likely being calculated incorrectly or omitted, causing this specific error?

  • In a language model that uses separate vectors for token identity, position, and sentence membership, the final input vector for a token is created by concatenating these three component vectors end-to-end.

  • Debugging Sentence Pair Representations

  • Segment Embedding

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  • Example of Input Embedding Composition for a Sentence Pair

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