Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
📰 ArXiv cs.AI
Learn to balance creativity and coherence in text generation using Top-H Decoding, a novel technique that adapts bounded entropy for improved results
Action Steps
- Apply Top-H Decoding to your text generation model to balance creativity and coherence
- Configure the entropy bounds to control the trade-off between diversity and logical coherence
- Test the performance of Top-H Decoding against existing truncated sampling techniques
- Compare the results of Top-H Decoding with temperature scaling, top-p sampling, and min-p sampling
- Fine-tune your model using Top-H Decoding to achieve optimal results
Who Needs to Know This
NLP engineers and researchers working on large language models can benefit from this technique to improve text generation quality
Key Insight
💡 Top-H Decoding offers a novel approach to balancing creativity and coherence in text generation by adapting bounded entropy
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📚 Improve text generation with Top-H Decoding! Balance creativity and coherence with bounded entropy 🤖
Key Takeaways
Learn to balance creativity and coherence in text generation using Top-H Decoding, a novel technique that adapts bounded entropy for improved results
Full Article
Title: Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
Abstract:
arXiv:2509.02510v2 Announce Type: replace-cross Abstract: Large language models (LLMs), despite their impressive performance across a wide range of tasks, often struggle to balance two competing objectives in open-ended text generation: fostering diversity and creativity while preserving logical coherence. Existing truncated sampling techniques, including temperature scaling, top-\$p\$ (nucleus) sampling, and min-\$p\$ sampling, aim to manage this trade-off. However, they exhibit limitations, pa
Abstract:
arXiv:2509.02510v2 Announce Type: replace-cross Abstract: Large language models (LLMs), despite their impressive performance across a wide range of tasks, often struggle to balance two competing objectives in open-ended text generation: fostering diversity and creativity while preserving logical coherence. Existing truncated sampling techniques, including temperature scaling, top-\$p\$ (nucleus) sampling, and min-\$p\$ sampling, aim to manage this trade-off. However, they exhibit limitations, pa
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