7 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…
Post-training makes large language models less human-like
Marcel Binz, Elif Akata, Abdullah Almaatouq +76
Large language models (LLMs) are increasingly used as surrogates for human participants, but it remains unclear which models best capture human behavior and why. To address this, w…
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…
On Emergent Social World Models -- Evidence for Functional Integration of Theory of Mind and Pragmatic Reasoning in Language Models
Polina Tsvilodub, Jan-Felix Klumpp, Amir Mohammadpour +2
This paper investigates whether LMs recruit shared computational mechanisms for general Theory of Mind (ToM) and language-specific pragmatic reasoning in order to contribute to the…
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.…