If the FFN Sees One Token at a Time, Why Doesn’t the Sentence Get Lost?

2026-06-22 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

The article addresses a common question regarding Transformer architecture: how the Feed-Forward Network (FFN) preserves a token's position, meaning, and grammatical role despite processing each token's vector independently. This "position-wise" processing within each Transformer layer often leads to the correct intuition that context should be lost. However, the text reassures that this intuition would only hold true if the FFN were the sole component and if token vectors were mere dictionary definitions. The preservation of context is attributed to subtle, beautiful mechanisms beyond the FFN, which are crucial for understanding how Transformers maintain sentence-level understanding.

Key takeaway

For AI Students or Machine Learning Engineers seeking a deeper understanding of Transformer mechanics, recognizing that the FFN's position-wise processing is only one part of context preservation is crucial. Your intuition about context loss is valid if considering the FFN in isolation; however, the full Transformer architecture employs additional, subtle mechanisms to maintain positional and semantic information. This insight should encourage you to explore how positional encoding and attention mechanisms contribute to a token's contextual understanding.

Key insights

Transformers preserve token context despite position-wise FFN processing through subtle mechanisms beyond the FFN itself.

Principles

• Position-wise FFN processing alone would lose context.
• Context preservation relies on more than just the FFN.

Topics

• Transformers
• Feed-Forward Networks
• Positional Encoding
• Tokenization
• Contextualization
• Natural Language Processing

Best for: AI Student, Machine Learning Engineer, AI Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by NLP on Medium.