Row 5736

Row ID: 5736 | Dataset Entry | Axioma AXP Content Repository

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**Authors**: Javier Ferrando (UPC), Gabriele Sarti (RUG), Arianna Bisazza (RUG), Marta Costa-jussà (Meta)

**Paper:** [https://arxiv.org/abs/2405.00208](https://arxiv.org/abs/2405.00208)

**Abstract:**

>The rapid progress of research aimed at interpreting the inner workings of advanced language models has highlighted a need for contextualizing the insights gained from years of work in this area. This primer provides a concise technical introduction to the current techniques used to interpret the inner workings of Transformer-based language models, focusing on the generative decoder-only architecture. We conclude by presenting a comprehensive overview of the known internal mechanisms implemented by these models, uncovering connections across popular approaches and active research directions in this area.

https://preview.redd.it/57y44wwdn6yc1.png?width=1486&format=png&auto=webp&s=7b7fb38a59f3819ce0d601140b1e031b98c17183

FieldValue
text **Authors**: Javier Ferrando (UPC), Gabriele Sarti (RUG), Arianna Bisazza (RUG), Marta Costa-jussà (Meta) **Paper:** [https://arxiv.org/abs/2405.00208](https://arxiv.org/abs/2405.00208) **Abstract:** >The rapid progress of research aimed at interpreting the inner workings of advanced language models has highlighted a need for contextualizing the insights gained from years of work in this area. This primer provides a concise technical introduction to the current techniques used to interpret th…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-03
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Raw Record

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  "text": "**Authors**: Javier Ferrando (UPC), Gabriele Sarti (RUG), Arianna Bisazza (RUG), Marta Costa-jussà (Meta)\n\n**Paper:** [https://arxiv.org/abs/2405.00208](https://arxiv.org/abs/2405.00208)\n\n**Abstract:**\n\n>The rapid progress of research aimed at interpreting the inner workings of advanced language models has highlighted a need for contextualizing the insights gained from years of work in this area. This primer provides a concise technical introduction to the current techniques used to interpret the inner workings of Transformer-based language models, focusing on the generative decoder-only architecture. We conclude by presenting a comprehensive overview of the known internal mechanisms implemented by these models, uncovering connections across popular approaches and active research directions in this area.\n\n\n\nhttps://preview.redd.it/57y44wwdn6yc1.png?width=1486&format=png&auto=webp&s=7b7fb38a59f3819ce0d601140b1e031b98c17183\n\n",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-03",
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