Driving and suppressing the human language network using large language models
Transformer models such as GPT generate human-like language and are predictive of
human brain responses to language. Here, using functional-MRI-measured brain responses …
human brain responses to language. Here, using functional-MRI-measured brain responses …
Do large language models know what humans know?
Humans can attribute beliefs to others. However, it is unknown to what extent this ability
results from an innate biological endowment or from experience accrued through child …
results from an innate biological endowment or from experience accrued through child …
Large language models demonstrate the potential of statistical learning in language
P Contreras Kallens… - Cognitive …, 2023 - Wiley Online Library
To what degree can language be acquired from linguistic input alone? This question has
vexed scholars for millennia and is still a major focus of debate in the cognitive science of …
vexed scholars for millennia and is still a major focus of debate in the cognitive science of …
Event knowledge in large language models: the gap between the impossible and the unlikely
Word cooccurrence patterns in language corpora contain a surprising amount of
conceptual knowledge. Large language models (LLMs), trained to predict words in context …
conceptual knowledge. Large language models (LLMs), trained to predict words in context …
Lexical-semantic content, not syntactic structure, is the main contributor to ANN-brain similarity of fMRI responses in the language network
Abstract Representations from artificial neural network (ANN) language models have been
shown to predict human brain activity in the language network. To understand what aspects …
shown to predict human brain activity in the language network. To understand what aspects …
Can language models handle recursively nested grammatical structures? A case study on comparing models and humans
A Lampinen - Computational Linguistics, 2024 - direct.mit.edu
How should we compare the capabilities of language models (LMs) and humans? In this
article, I draw inspiration from comparative psychology to highlight challenges in these …
article, I draw inspiration from comparative psychology to highlight challenges in these …
How to plant trees in language models: Data and architectural effects on the emergence of syntactic inductive biases
Accurate syntactic representations are essential for robust generalization in natural
language. Recent work has found that pre-training can teach language models to rely on …
language. Recent work has found that pre-training can teach language models to rely on …
Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding
Current Large Language Models (LLMs) are unparalleled in their ability to generate
grammatically correct, fluent text. LLMs are appearing rapidly, and debates on LLM …
grammatically correct, fluent text. LLMs are appearing rapidly, and debates on LLM …
[HTML][HTML] Surprisal from language models can predict ERPs in processing predicate-argument structures only if enriched by an Agent Preference principle
Abstract Language models based on artificial neural networks increasingly capture key
aspects of how humans process sentences. Most notably, model-based surprisals predict …
aspects of how humans process sentences. Most notably, model-based surprisals predict …
Why linguistics will thrive in the 21st century: A reply to Piantadosi (2023)
We present a critical assessment of Piantadosi's (2023) claim that" Modern language
models refute Chomsky's approach to language," focusing on four main points. First, despite …
models refute Chomsky's approach to language," focusing on four main points. First, despite …