Encoder-Decoder Architecture
A neural network design where an encoder compresses input data into a fixed representation and a decoder generates output from that representation, widely used in translation and summarization.
The encoder-decoder architecture was pivotal in the development of sequence-to-sequence models for machine translation. The original transformer (2017) used an encoder-decoder structure, though modern LLMs like GPT use decoder-only architectures. Encoder-decoder models like T5 and BART excel at tasks that require understanding input before generating output, such as summarization and translation. Google's T5 model demonstrated that framing all NLP tasks as text-to-text problems with an encoder-decoder could achieve state-of-the-art results.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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