Semantic uncertainty in advanced decoding methods for LLM generation

2025-06-17

AI Summary

This study investigates how structured decoding strategies—specifically speculative sampling and chain-of-thought (CoT) decoding—affect semantic uncertainty in large language model (LLM) outputs. We propose a prediction-entropy-based metric to quantify semantic uncertainty and conduct multi-task evaluation using Pass@2, ROUGE, and reference alignment. Our key findings challenge the conventional diversity–accuracy trade-off: structured decoding simultaneously enhances both semantic diversity and predictive confidence. Specifically, CoT decoding improves Pass@2 by 48.8% in code generation, while speculative sampling significantly boosts ROUGE scores in summarization while preserving moderate diversity. This work provides novel empirical evidence and an integrated evaluation framework for controllable, trustworthy LLM generation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Investigates semantic uncertainty in LLM outputs across decoding methods
• Analyzes decoding strategies' impact on output diversity and reliability
• Challenges trade-offs between diversity and accuracy in model outputs

Innovation

• Analyzes semantic uncertainty in LLM decoding methods
• Shows CoT decoding improves semantic diversity and accuracy
• Demonstrates speculative sampling enhances summarization performance