Likelihood Ranking doesn't Scale Like Prompting in LLMs
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....
| Authors | Alessandro Bondielli, Lucia Passaro, Davide Bacciu et al. |
|---|---|
| Primary category | cs.CL |
| Published | 24 September 2026 |
| Last updated at source | 25 September 2026 |
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