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Interfaces for Explaining Transformer Language Models

Named practitioner synthesis

#248

T3digested
Topic
Translation NLP
First seen
2026-07-16 19:08:01
Last seen
2026-07-16 19:08:01

Source raw items (1)

  • Blog / Newsletter2026-07-16 19:07:22
    Interfaces for Explaining Transformer Language Models

    Interfaces for exploring transformer language models by looking at input saliency and neuron activation. Explorable #1: Input saliency of a list of countries generated by a language model Tap or hover over the output tokens: Explorable #2: Neuron activation analysis reveals four groups of neurons, each is associated with generating a certain type of token Tap or hover over the sparklines on the left to isolate a certain factor: The Transformer architecture has been powering a number of the recent advances in NLP. A breakdown of this architecture is provided here . Pre-trained language models based on the architecture, in both its auto-regressive (models that use their own output as input to next time-steps and that process tokens from left-to-right, like GPT2) and denoising (models trained by corrupting/masking the input and that process tokens bidirectionally, like BERT) variants continue to push the envelope in various tasks in NLP and, more recently, in computer vision. Our understa