Likelihood Ranking doesn't Scale Like Prompting in LLMs

Abstract:LLM evaluation is commonly performed either by prompting models to produce answers or by scoring candidate outputs with likelihood-based metrics. In multiple-choice QA, however, standard likelihood-based scoring is still conditioned on the question and answer set, and can therefore leverage the same task-conditioned answer-selection interface used in prompting. We study a complementary protocol based on likelihood ranking of declarative statements constructed from the same question--answer pairs. Across 95 decoder-only models, ranging from 0.1B to 104B parameters, and 10 MCQA datasets, we find a systematic divergence between declarative-statement likelihood ranking and prompted answering. Statement-likelihood accuracy remains comparatively stable across scale, whereas prompted answering improves sharply with scale and instruction-tuning. These results suggest that likelihood preferences over controlled declarative alternatives and task-conditioned answer selection probe distinct aspects of model behavior, and should not be treated as interchangeable.

Computation and Language (cs.CL)

arXiv:2609.29390 [cs.CL]

(or arXiv:2609.29390v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.29390 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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