When are likely answers right? On Sequence Probability and Correctness in LLMs
Source published ·Modelwire updated
Original coverage: arXiv cs.LG ↗·How Modelwire adds context
The development
A new study quantifies the relationship between sequence probability and correctness across decoding methods, models, and benchmarks, revealing when LLMs' internal likelihood estimates actually predict accurate outputs. The research tests this alignment at multiple granularities: across decoding strategies, hyperparameter tuning, individual prompt-answer pairs, and repeated generations. This work matters because most modern sampling and beam-search techniques assume higher probability correlates with better answers, yet the assumption remains largely unvalidated at scale. Understanding where this breaks down could reshape how practitioners select decoding methods and inform better confidence calibration for production systems.
Modelwire’s AI-generated summary of coverage from arXiv cs.LG.
Modelwire analysis
ExplainerOur AI-generated reading of the wider context and the next developments to watch.
The buried issue here is calibration, not just decoding strategy. Most production systems treat high sequence probability as a proxy for reliability, and this paper is one of the first to stress-test that proxy systematically across benchmarks rather than on a single task or model family.
This connects most directly to the RiVER paper covered the same day ('Reinforcement Learning without Ground-Truth Solutions can Improve LLMs'), which sidesteps the probability-correctness problem entirely by using execution feedback as reward rather than relying on model confidence. That framing is telling: if RL practitioners are already building around the assumption that internal likelihood is an unreliable training signal, this paper provides the empirical scaffolding that explains why. The two pieces together suggest a quiet consensus forming around the limits of sequence probability as a meaningful guide, whether for training or inference.
Watch whether any of the major inference frameworks (vLLM, TGI) incorporate calibration-aware decoding options within the next two release cycles. If they do, this line of research is being treated as actionable; if not, it stays academic.
This interpretation is generated from the summary above and available source metadata. Our methodology · Report an error
MentionsLarge Language Models · Decoding Methods · Sequence Probability · Beam Search · Token-level Sampling
How this coverage is produced
Modelwire uses AI to generate summaries and context from source headlines, snippets, and selected archive coverage. Automated checks do not verify every claim, and items are not routinely reviewed by a person before publication. Zacaria Solis operates the site. Read the linked source for the full evidence and report errors through our corrections process.
Modelwire summarizes, we don’t republish. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.