6 papers
Estimating Surprisal from the Speech Signal
Zachary Hopton, Samuel Kiegeland, Francesco Ignazio Re +4
Although language is predominantly spoken, empirical tests of surprisal theory rely almost entirely on written text. In practice, surprisal is estimated using language models (LMs)…
Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics
Zachary Hopton, Francesco Ignazio Re, Samuel Kiegeland +3
Contextual and unigram surprisal are standard predictors of reading time in computational psycholinguistics. While contextual surprisal is typically derived from neural language mo…
Estimating Great Expectations Under Autoregressive Language Models with Potentials
Francesco I. Re, Shubhangi Ghosh, Tim Vieira +1
Many applications of language models require estimating the expectation of a test functional under the model. Obtaining precise estimates can be computationally expensive. We show…
Learning to Reason Efficiently with A* Post-Training
Andreas Opedal, Francesco Ignazio Re, Abulhair Saparov +3
Many applications of large language models (LLMs) require deductive reasoning, yet models frequently produce incorrect or redundant inference steps. We frame natural language infer…
Probing for Reading Times
Eleftheria Tsipidi, Samuel Kiegeland, Francesco Ignazio Re +4
Probing has shown that language model representations encode rich linguistic information, but it remains unclear whether they also capture cognitive signals about human processing.…
A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior
Francesco Ignazio Re, Andreas Opedal, Glib Manaiev +2
Reading is a process that unfolds across space and time, alternating between fixations where a reader focuses on a specific point in space, and saccades where a reader rapidly shif…