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Paper Podcast
"Think before you speak: Training Language Models With Pause Tokens"
Delayed token generation with Pauses in pretraining with and finetuning unlock latent Transformer capabilities on diverse tasks. 🤔
**Original Problem** 🔍:
Transformer-based language models generate tokens in immediate succession, with the (K+1)th token based on K hidden vectors per layer. This imposes an arbitrary computational constraint limiting the number of operations for the next token to the number of tokens seen so far.
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**Key Insights from this Paper** 💡:
• Appending learnable "pause" tokens allows more computation before output
• Training and inference with pauses enables tapping into untapped model capacity
• Benefits emerge when pauses are used in both pretraining and finetuning
• Optimal number of pauses varies by downstream task
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The Podcast is generated with Google's illuminate, the tool trained on AI & science-related Arxiv papers.
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👇 All the Paper Podcasts are also available on my YouTube channel playlist 👇
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