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Experimental Pragmatics with Machines: Testing LLM Predictions for the Inferences of Plain and Embedded Disjunctions
Human communication is based on a variety of inferences that we draw from sentences, often going beyond what is literally said.
Predictions from language models for multiple-choice tasks are not robust under variation of scoring methods
This paper systematically compares different methods of deriving item-level predictions of language models for multiple-choice tasks.
Evaluating Pragmatic Abilities of Image Captioners on A3DS
Evaluating grounded neural language model performance with respect to pragmatic qualities like the trade off between truthfulness, contrastivity and overinformativity of generated utterances remains a challenge in absence of data collected from humans.
Overinformative Question Answering by Humans and Machines
When faced with a polar question, speakers often provide overinformative answers going beyond a simple "yes" or "no".
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