21 citations · 23 across the 14 of their papers we have counts for
4 papers · 1 filter
Generalized Measures of Anticipation and Responsivity in Online Language Processing
Mario Giulianelli, Andreas Opedal, Ryan Cotterell
We introduce a generalization of classic information-theoretic measures of predictive uncertainty in online language processing, based on the simulation of expected continuations o…
MathGAP: Out-of-Distribution Evaluation on Problems with Arbitrarily Complex Proofs
Andreas Opedal, Haruki Shirakami, Bernhard Schölkopf +2
Large language models (LLMs) can solve arithmetic word problems with high accuracy, but little is known about how well they generalize to more complex problems. This is difficult t…
On the Role of Context in Reading Time Prediction
Andreas Opedal, Eleanor Chodroff, Ryan Cotterell +1
We present a new perspective on how readers integrate context during real-time language comprehension. Our proposals build on surprisal theory, which posits that the processing eff…
Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?
Andreas Opedal, Alessandro Stolfo, Haruki Shirakami +5
There is increasing interest in employing large language models (LLMs) as cognitive models. For such purposes, it is central to understand which properties of human cognition are w…