Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs
Publication Date
5-27-2026
Document Type
Contribution to a Book
Publication Title
Intelligent Systems Reference Library
Volume
283
DOI
10.1007/978-3-032-13497-4_17
First Page
425
Last Page
484
Abstract
This chapter explores advancements in decoding strategies for large language models (LLMs), focusing on enhancing the Locally Typical Sampling (LTS) algorithm. Traditional decoding methods, such as top-k and nucleus sampling, often struggle to balance fluency, diversity, and coherence in text generation. To address these challenges, Adaptive Semantic-Aware Typicality Sampling (ASTS) is proposed as an improved version of LTS, incorporating dynamic entropy thresholding, multi-objective scoring, and reward-penalty adjustments. ASTS ensures contextually coherent and diverse text generation while maintaining computational efficiency. Its performance is evaluated across multiple benchmarks, including story generation and abstractive summarization, using metrics such as perplexity, MAUVE, and diversity scores. Experimental results demonstrate that ASTS outperforms existing sampling techniques by reducing repetition, enhancing semantic alignment, and improving fluency.
Keywords
Adaptive Semantic-Aware Typicality Sampling (ASTS), Decoding strategies, Entropy-based sampling, Large language models (LLMs), Locally typical sampling, Multi-objective scoring
Department
Computer Science
Recommended Citation
Jaydip Sen, Saptarshi Sengupta, and Subhasis Dasgupta. "Advancing Decoding Strategies: Enhancements in Locally Typical Sampling for LLMs" Intelligent Systems Reference Library (2026): 425-484. https://doi.org/10.1007/978-3-032-13497-4_17
