Dissociating language and thought in large language models
Large language models (LLMs) have come closest among all models to date to mastering
human language, yet opinions about their linguistic and cognitive capabilities remain split …
human language, yet opinions about their linguistic and cognitive capabilities remain split …
The relational bottleneck as an inductive bias for efficient abstraction
A central challenge for cognitive science is to explain how abstract concepts are acquired
from limited experience. This has often been framed in terms of a dichotomy between …
from limited experience. This has often been framed in terms of a dichotomy between …
From word models to world models: Translating from natural language to the probabilistic language of thought
How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …
meaning from language--and how can we leverage a theory of linguistic meaning to build …
[PDF][PDF] Findings of the BabyLM Challenge: Sample-efficient pretraining on developmentally plausible corpora
Children can acquire language from less than 100 million words of input. Large language
models are far less data-efficient: they typically require 3 or 4 orders of magnitude more data …
models are far less data-efficient: they typically require 3 or 4 orders of magnitude more data …
Systematic testing of three Language Models reveals low language accuracy, absence of response stability, and a yes-response bias
V Dentella, F Günther… - Proceedings of the …, 2023 - National Acad Sciences
Humans are universally good in providing stable and accurate judgments about what forms
part of their language and what not. Large Language Models (LMs) are claimed to possess …
part of their language and what not. Large Language Models (LMs) are claimed to possess …
Transmission versus truth, imitation versus innovation: What children can do that large language and language-and-vision models cannot (yet)
Much discussion about large language models and language-and-vision models has
focused on whether these models are intelligent agents. We present an alternative …
focused on whether these models are intelligent agents. We present an alternative …
On the importance of severely testing deep learning models of cognition
Researchers studying the correspondences between Deep Neural Networks (DNNs) and
humans often give little consideration to severe testing when drawing conclusions from …
humans often give little consideration to severe testing when drawing conclusions from …
Event knowledge in large language models: the gap between the impossible and the unlikely
Word co‐occurrence 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 …
Assessing the strengths and weaknesses of Large Language Models
S Lappin - Journal of Logic, Language and Information, 2024 - Springer
The transformers that drive chatbots and other AI systems constitute large language models
(LLMs). These are currently the focus of a lively discussion in both the scientific literature …
(LLMs). These are currently the focus of a lively discussion in both the scientific literature …
[HTML][HTML] Language models and psychological sciences
Large language models (LLMs) are demonstrating impressive performance on many
reasoning and problem-solving tasks from cognitive psychology. When tested, their …
reasoning and problem-solving tasks from cognitive psychology. When tested, their …