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Related Questions
- How do different activation functions (e.g. ReLU, Swish, gelu) impact the gradient flow and model training stability in transformer-based LLMs?
- What are the advantages and disadvantages of using leaky ReLU compared to other activation functions in transformer-based LLMs?
- How does the choice of activation function influence the attention mechanism in transformer-based LLMs, and what are the implications for contextual understanding?
- What are the empirical results on the effect of activation functions on the performance of transformer-based LLMs in downstream tasks like language translation and text summarization?
- How does the choice of activation function interact with other architectural components in transformer-based LLMs, such as layer normalization and residual connections?
- What are the implications of using activation functions like sigmoid or tanh in transformer-based LLMs, and how do they compare to more commonly used functions like ReLU or gelu?
- Can you discuss the trade-offs between different activation functions in terms of model capacity, training speed, and inference efficiency in transformer-based LLMs?
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