Semantic uncertainty in advanced decoding methods for LLM generation

TL;DR

This paper explores how different advanced decoding strategies like CoT and speculative sampling influence the semantic diversity and reliability of LLM outputs across tasks, revealing that structured exploration can enhance both diversity and accuracy.

Contribution

It provides a comprehensive analysis of semantic uncertainty in LLM decoding, demonstrating that structured methods can improve output quality and diversity simultaneously.

Findings

CoT decoding increases semantic diversity but lowers predictive entropy.

Speculative sampling achieves higher ROUGE scores in summarization.

Structured decoding methods can enhance both diversity and accuracy.

Abstract

This study investigates semantic uncertainty in large language model (LLM) outputs across different decoding methods, focusing on emerging techniques like speculative sampling and chain-of-thought (CoT) decoding. Through experiments on question answering, summarization, and code generation tasks, we analyze how different decoding strategies affect both the diversity and reliability of model outputs. Our findings reveal that while CoT decoding demonstrates higher semantic diversity, it maintains lower predictive entropy, suggesting that structured exploration can lead to more confident and accurate outputs. This is evidenced by a 48.8% improvement in code generation Pass@2 rates, despite lower alignment with reference solutions. For summarization tasks, speculative sampling proved particularly effective, achieving superior ROUGE scores while maintaining moderate semantic diversity. Our results challenge conventional assumptions about trade-offs between diversity and accuracy in language model outputs, demonstrating that properly structured decoding methods can increase semantic exploration while maintaining or improving output quality. These findings have significant implications for deploying language models in practical applications where both reliability and diverse solution generation are crucial.