arXiv:2606.27359v1 Announce Type: cross Abstract: Many decoding methods for large language models can be understood as shifting probability mass toward outputs that are more likely under the model, either locally at the token level or globally at the sequence level. Therefore, their success depends on a fundamental question: when does sequence probability, that is, the conditional probability of a continuation given a prompt, actually align with correctness? In this paper, we set out to quantify this relationship across decoding methods, models, and benchmarks at four levels: across decoding m
This paper addresses a fundamental question central to current LLM development as the technology matures, exploring the relationship between model-generated probability and factual correctness.
Understanding when sequence probability aligns with correctness in LLMs is crucial for developing more reliable AI systems and for accurately evaluating their performance, particularly in high-stakes applications.
Confidence in LLM outputs can now be more reliably assessed by quantifying the link between probability and correctness, enabling better decision-making in model selection and deployment.
- · AI researchers
- · LLM developers
- · Enterprises deploying LLMs
- · Researchers relying on superficial LLM evaluation metrics
- · Applications where correctness is paramount but not well-validated
Increased understanding of LLM reliability and limitations through empirical quantification of probability-correctness alignment.
Improved decoding strategies and model architectures that prioritize factual accuracy over mere token probability.
Enhanced trust in AI systems due to more robust methods for correlating LLM outputs with verifiable correctness, leading to broader adoption in sensitive domains.
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Read at arXiv cs.LG