565 citations · 783 across the 29 of their papers we have counts for
8 papers · 1 filter
From Language Models over Tokens to Language Models over Characters
Tim Vieira, Ben LeBrun, Mario Giulianelli +5
Modern language models are internally -- and mathematically -- distributions over strings rather than strings, posing numerous challenges for programm…
Towards a Similarity-adjusted Surprisal Theory
Clara Meister, Mario Giulianelli, Tiago Pimentel
Surprisal theory posits that the cognitive effort required to comprehend a word is determined by its contextual predictability, quantified as surprisal. Traditionally, surprisal th…
Surprise! Uniform Information Density Isn't the Whole Story: Predicting Surprisal Contours in Long-form Discourse
Eleftheria Tsipidi, Franz Nowak, Ryan Cotterell +3
The Uniform Information Density (UID) hypothesis posits that speakers tend to distribute information evenly across linguistic units to achieve efficient communication. Of course, i…
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…
On the Proper Treatment of Tokenization in Psycholinguistics
Mario Giulianelli, Luca Malagutti, Juan Luis Gastaldi +3
Language models are widely used in computational psycholinguistics to test theories that relate the negative log probability (the surprisal) of a region of interest (a substring of…
Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren +3
The ability to communicate uncertainty, risk, and limitation is crucial for the safety of large language models. However, current evaluations of these abilities rely on simple cali…
LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks
Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi +17
There is an increasing trend towards evaluating NLP models with LLMs instead of human judgments, raising questions about the validity of these evaluations, as well as their reprodu…
LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models
Ivar Frisch, Mario Giulianelli
While both agent interaction and personalisation are vibrant topics in research on large language models (LLMs), there has been limited focus on the effect of language interaction…