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Balancing Randomness and Coherence in Token Sampling
Sampling-based decoding methods like Top- and Top- restrict the selection pool to a smaller subset of high-probability candidates, effectively striking a balance between output randomness and text coherence. This restriction enables the large language model to generate more diverse sequences while maintaining relevance and fluency. The hyperparameters and must be tuned carefully: excessively small values yield highly deterministic outputs that closely resemble greedy decoding, whereas overly large values can cause the model to produce degenerate outputs.
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Foundations of Large Language Models
Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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