Text Production and Comprehension by Human and Artificial Intelligence: Interdisciplinary Workshop Report
Emily Dux Speltz

TL;DR
This interdisciplinary workshop report explores how large language models relate to human cognition in text comprehension and production, highlighting insights, limitations, and future research directions for AI-human collaboration.
Contribution
It synthesizes interdisciplinary perspectives to understand the relationship between AI language models and human cognitive processes in text tasks.
Findings
LLMs can provide insights into human language processing
Fine-tuning LLMs with human feedback improves alignment with human cognition
Human-AI collaboration presents both opportunities and challenges in language tasks
Abstract
This report synthesizes the outcomes of a recent interdisciplinary workshop that brought together leading experts in cognitive psychology, language learning, and artificial intelligence (AI)-based natural language processing (NLP). The workshop, funded by the National Science Foundation, aimed to address a critical knowledge gap in our understanding of the relationship between AI language models and human cognitive processes in text comprehension and composition. Through collaborative dialogue across cognitive, linguistic, and technological perspectives, workshop participants examined the underlying processes involved when humans produce and comprehend text, and how AI can both inform our understanding of these processes and augment human capabilities. The workshop revealed emerging patterns in the relationship between large language models (LLMs) and human cognition, with highlights on…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeurobiology of Language and Bilingualism · Language, Metaphor, and Cognition · Text Readability and Simplification
