Language models (LMs) are increasingly used for tasks that require reasoning about hidden variables from a few observations, for which Bayesian inference is the normatively correct solution. While supervised fine-tuning of an LM on the outputs of an optimal $\textit{Bayesian}$ model leads to near-Bayesian behavior, sta...
Polina Tsvilodub, Andreas Waldis, Lin-Lu Qiu et al.· 0 citations
This work takes inspiration from a resource-rational perspective on human cognition and introduces a novel GRPO reward that encourages efficient reasoning about whether the task contains all the information needed to solve it, leading to human-like abstention performance gains.
Polina Tsvilodub, M. Höth, Michael Franke et al.· 0 citations
CogGym is introduced, a scalable, unified framework grounded in cognitive science for systematically comparing model and human behavior on matched experimental trials, and intends for CogGym to provide a living evaluation framework that continually incorporates new cognitive science experiments to characterize where mo...
Lance Ying, Jinzhou Wu, Y. Wang et al.· 0 citations
The results show that LLMs systematically adapt their responses to align with prompt framing, even in factual contexts, which suggests that prompt framing can outweigh factual consistency in model responses.
Mudar Adas, Polina Tsvilodub, Michael Franke et al.· 0 citations
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