Article

Expert-adapted language models improve the fit to reading times

2023-07-31

Abstract excerpt

<p>The concept of surprisal refers to the predictability of a word based on its context. Surprisal is known to be predictive of human processing difficulty and is usually estimated by language models. However, because humans differ in their linguistic experience, they also differ in the actual processing difficulty they experience with a given word or sentence. We investigate whether models that are similar to the...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: f209ae97-e28a-54bf-9b6f-7e2e42c11642
DOI: 10.31234/osf.io/dc8y6

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Expert-adapted language models improve the fit to reading timesDOI 10.31234/osf.io/dc8y6Read

Optimizing Predictive Metrics for Human Reading BehaviorSemantic proximity Read

Large Language Models and the Wisdom of Small CrowdsSemantic proximity Read

Comprehension effort as the cost of inferenceSemantic proximity Read

When ChatGPT-4o Is (Less) Human-Like: Preliminary Subjective Rating Tests for Psycholinguistic ResearchSemantic proximity Read

Evaluating information-theoretic measures of word prediction in naturalistic sentence readingSemantic proximity Read

Character-based Surprisal as a Model of Reading Difficulty in the Presence of ErrorsSemantic proximity Read

Hierarchical Resource Rationality Explains Human Reading BehaviorSemantic proximity Read

Hierarchical Resource Rationality Explains Human Reading BehaviorSemantic proximity Read

Crowdsourced and AI-generated Age of Acquisition (AoA) Norms for Vocabulary in Print: Extending the Kuperman et al. (2012) normsSemantic proximity Read

Crowdsourced and AI-generated Age of Acquisition (AoA) Norms for Vocabulary in Print: Extending the Kuperman et al. (2012) normsSemantic proximity Read

Modeling Task Effects in Human Reading with Neural Network-based AttentionSemantic proximity Read

Multimodality and Attention Increase Alignment in Natural Language Prediction Between Humans and Computational ModelsSemantic proximity Read