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Found 4 papers, 3 papers with code
Comparing Plausibility Estimates in Base and Instruction-Tuned Large Language Models
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.
A Better Way to Do Masked Language Model Scoring
However, for masked language models (MLMs), there is no direct way to estimate the log-likelihood of a sentence.
Event knowledge in large language models: the gap between the impossible and the unlikely
Overall, our results show that important aspects of event knowledge naturally emerge from distributional linguistic patterns, but also highlight a gap between representations of possible/impossible and likely/unlikely events.
The neural architecture of language: Integrative modeling converges on predictive processing
The neuroscience of perception has recently been revolutionized with an integrative modeling approach in which computation, brain function, and behavior are linked across many datasets and many computational models.
