5 papers
Confirming Our Biases? Evaluating the Capabilities, Risks, and Societal Impact of Large Language Models
Mudar Adas, Polina Tsvilodub, Michael Franke +1
It is well established that large language models (LLMs) are sensitive to prompt framing, reflecting patterns in their training data or prior prompts. In this study, we investigate…
Computational models of pragmatic reasoning with flexible generation of meaning and expression alternatives
Polina Tsvilodub, Fausto Carcassi, Michael Franke
Pragmatic language use requires reasoning about alternatives: the alternative expressions a speaker might have chosen, or the alternative interpretations a listener might entertain…
Act or Clarify? Modeling Sensitivity to Uncertainty and Cost in Communication
Polina Tsvilodub, Karl Mulligan, Todd Snider +2
When deciding how to act under uncertainty, agents may choose to act to reduce uncertainty or they may act despite that uncertainty. In communicative settings, an important way of…
Integrating Neural and Symbolic Components in a Model of Pragmatic Question-Answering
Polina Tsvilodub, Robert D. Hawkins, Michael Franke
Computational models of pragmatic language use have traditionally relied on hand-specified sets of utterances and meanings, limiting their applicability to real-world language use.…
Non-literal Understanding of Number Words by Language Models
Polina Tsvilodub, Kanishk Gandhi, Haoran Zhao +3
Humans naturally interpret numbers non-literally, effortlessly combining context, world knowledge, and speaker intent. We investigate whether large language models (LLMs) interpret…