6 papers
On the Proper Treatment of Units in Surprisal Theory
Samuel Kiegeland, Vésteinn Snæbjarnarson, Tim Vieira +1
Surprisal theory links human processing effort to the predictability of an upcoming linguistic unit, but empirical work often leaves the notion of a unit underspecified. In practic…
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.…
Ensembling Language Models with Sequential Monte Carlo
Robin Shing Moon Chan, Tianyu Liu, Samuel Kiegeland +5
Practitioners have access to an abundance of language models and prompting strategies for solving many language modeling tasks; yet prior work shows that modeling performance is hi…
Transducing Language Models
Vésteinn Snæbjarnarson, Samuel Kiegeland, Tianyu Liu +3
Modern language models define distributions over strings, but downstream tasks often require different output formats. For instance, a model that generates byte-pair strings does n…
The Harmonic Structure of Information Contours
Eleftheria Tsipidi, Samuel Kiegeland, Franz Nowak +5
The uniform information density (UID) hypothesis proposes that speakers aim to distribute information evenly throughout a text, balancing production effort and listener comprehensi…
Reverse-Engineering the Reader
Samuel Kiegeland, Ethan Gotlieb Wilcox, Afra Amini +2
Numerous previous studies have sought to determine to what extent language models, pretrained on natural language text, can serve as useful models of human cognition. In this paper…