  Instruction-tuned LLMs can respond to explicit queries formulated as prompts,
which greatly facilitates interaction with human users. However, prompt-based
approaches might not always be able to tap into the wealth of implicit
knowledge acquired by LLMs during pre-training. This paper presents a
comprehensive study of ways to evaluate semantic plausibility in LLMs. We
compare base and instruction-tuned LLM performance on an English sentence
plausibility task via (a) explicit prompting and (b) implicit estimation via
direct readout of the probabilities models assign to strings. Experiment 1
shows that, across model architectures and plausibility datasets, (i) log
likelihood ($\textit{LL}$) scores are the most reliable indicator of sentence
plausibility, with zero-shot prompting yielding inconsistent and typically poor
results; (ii) $\textit{LL}$-based performance is still inferior to human
performance; (iii) instruction-tuned models have worse $\textit{LL}$-based
performance than base models. In Experiment 2, we show that $\textit{LL}$
scores across models are modulated by context in the expected way, showing high
performance on three metrics of context-sensitive plausibility and providing a
direct match to explicit human plausibility judgments. Overall, $\textit{LL}$
estimates remain a more reliable measure of plausibility in LLMs than direct
prompting.
