Demystifying AI Models: Encoders, Decoders, and Embeddings

Understanding how artificial intelligence models (AI models) work is key for entrepreneurs looking to leverage them. Specifically, models like transformer-based language models use encoders, decoders, and embeddings in unique ways. This article will demystify these concepts to empower AI startups.

Encoders vs Decoders

Many AI models use an encoder-decoder architecture. This contains two components:

  • Encoder: Converts input data like text into a basic numeric representation called an encoding.
  • Decoder: Transforms the encoding into a new representation by passing it through multiple layers.

For example, in machine translation the encoder reads the source text and produces a generic encoding. The decoder then transforms that encoding into the target language text.

Decoder-only models like GPT-3 handle encoding and decoding combined within one component. The first decoder layer encodes the input, then deeper layers transform it further.

So encoder-decoder models separate the steps, while decoder-only models combine them. But both approaches incorporate encoding and deeper processing.

Embeddings – Structured Encodings of Meaning

An embedding is a specific type of encoding where the numeric representation has useful structure and properties.

For instance, embeddings map similar words like “happy” and “joyful” to similar vector representations. This captures semantic meaning within the encoding.

Transformer models like GPT create embeddings by passing text through multiple layers. Each layer encodes the input into a new vector representation.

The final output embedding incorporates all the layers of transformations to represent the full semantic meaning of the original text.

Use Cases – When To Use Encoders vs Embeddings

Should your AI startup use encoders or just embeddings? Here are some guidelines:

Encoders Help With:

  • Machine translation – Encoders build representation of full source sentence before translating.
  • Text classification – Encodes entire documents like news articles to determine topic.
  • Anomaly detection – Analyzes sequence data like credit card transactions to find outliers.

Embeddings Are Sufficient For:

  • Search/recommendations – Embedding queries or product descriptions provides semantics for matching.
  • Chatbots – Current user utterance embedding has enough context for next bot response.
  • Keyword spotting – Detecting keywords in audio with embeddings, without sequence info.

So in summary, use encoders when sequence information is critical. Use embeddings when localized semantics are enough.

Decoder-Only Models Advancements

Recent advances have enabled decoder-only models like GPT-3 to take on tasks previously requiring encoders:

  • Increased context lengths – GPT-3 has up to 2048 tokens of context, allowing sufficient conditioning on source texts for translation and other tasks.
  • Chunked processing – Long sequences like documents can be processed over multiple passes of shorter chunks.

So for many applications, startups can now use efficient decoder-only models instead of traditional encoder-decoders.

Key Takeaways for AI Startups

Here are the critical points for startups to consider when developing AI systems:

  • Encoder-decoder models separate encoding and decoding steps, while decoder-only models combine them.
  • Embeddings capture meaning by transforming input through multiple layers.
  • Use encoders when sequence order is critical, embeddings when localized semantics suffice.
  • Recent advances allow decoder-only models to handle tasks previously requiring encoders.
  • Carefully consider which architecture fits your use case based on sequence needs and semantics.

By understanding encoders, decoders and how they create embeddings, startups can develop more advanced and scalable AI solutions.

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