Top-W applies Wasserstein-regularized truncation on token-embedding geometry to create a closed-form optimal crop for LLM sampling that outperforms prior methods by up to 33.7% on GSM8K, GPQA, AlpacaEval, and MT-Bench.
Mirostat: A neural text decoding algorithm that directly controls perplexity
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Top-H decoding is a computationally efficient greedy algorithm for an entropy-constrained mass maximization problem that improves the creativity-coherence trade-off over min-p sampling in LLM text generation.
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Geometry-Aware Decoding with Wasserstein-Regularized Truncation and Mass Penalties for Large Language Models
Top-W applies Wasserstein-regularized truncation on token-embedding geometry to create a closed-form optimal crop for LLM sampling that outperforms prior methods by up to 33.7% on GSM8K, GPQA, AlpacaEval, and MT-Bench.
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Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
Top-H decoding is a computationally efficient greedy algorithm for an entropy-constrained mass maximization problem that improves the creativity-coherence trade-off over min-p sampling in LLM text generation.