When are likely answers right? On Sequence Probability and Correctness in LLMs

2026-06-25 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new study quantifies the relationship between sequence probability and correctness in large language models (LLMs) across various decoding methods, hyperparameters, and prompt-answer pairs. Researchers found that higher sequence probability often predicts correctness when comparing different prompt-answer pairs within a fixed dataset. However, this correlation does not reliably extend to decoding decisions; increasing sequence probability by altering hyperparameters or methods does not consistently improve accuracy. Furthermore, sequence probability proves to be an unreliable indicator of correctness for repeated responses generated from the same prompt. These findings offer crucial clarity on when decoding strategies can genuinely enhance LLM correctness and provide practical guidance for self-consistency and verifier-free self-improvement techniques.

Key takeaway

For ML Engineers and AI Scientists optimizing LLM outputs, you should reconsider relying solely on increasing sequence probability through decoding methods or hyperparameters to boost accuracy. While sequence probability can indicate correctness within a dataset, it does not reliably transfer to improving model performance via decoding changes. Focus your efforts on strategies that address the fundamental alignment of model likelihood with truth, rather than assuming higher probability always means better answers, especially for repeated generations.

Key insights

Higher sequence probability often predicts correctness within datasets, but not across decoding decisions or repeated responses.

Principles

• Sequence probability predicts correctness across prompt-answer pairs within fixed datasets.
• Increasing sequence probability via decoding changes does not reliably improve accuracy.
• Sequence probability is not a good correctness indicator for repeated responses to the same prompt.

In practice

• Re-evaluate decoding strategies.
• Inform self-consistency approaches.
• Guide verifier-free self-improvement.

Topics

• Large Language Models
• Decoding Methods
• Sequence Probability
• Model Correctness
• Self-consistency
• Verifier-free Self-improvement

Best for: Research Scientist, AI Engineer, AI Scientist, Machine Learning Engineer, NLP Engineer

Related on AIssential

• When are likely answers right? On Sequence Probability and Correctness in LLMs — Sequence probability reliably predicts correctness only within a fixed dataset, not across decoding methods or hyperparameters.
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• Adaptive Conformal Prediction for Improving Factuality of Generations by Large Language Models — Adaptive conformal prediction improves LLM factuality by calibrating uncertainty thresholds based on prompt characteristics, enhancing conditional coverage.
• Your LLM Is Guessing Ahead. Then It Checks Itself aka Speculative Decoding — Speculative decoding accelerates LLM inference by using a small model to guess tokens, verified by a large model in one pass.
• Understanding Data Temporality Impact on Large Language Models Pre-training — Temporally ordering pre-training data significantly improves LLM factual freshness and temporal precision without sacrificing general knowledge.
• How reliable are LLMs when it comes to playing dice? — Current LLMs struggle with probabilistic reasoning, especially on counterintuitive problems and when faced with subtle biases or misleading prompts.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.