ArticleScientific reports2024
Testing AI on language comprehension tasks reveals insensitivity to underlying meaning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Review
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- A S.C.O.R.E. framework for evaluating open-ended responses from large language models in healthcare.Cell reports. Medicine · 2026Article
- Evaluating large language model performance and reliability in scoring picture description tasks for neuropsychological assessment.PLOS digital health · 2026Article
- A Suite of LMs Comprehend Puzzle Statements as Well or Better Than Humans.Open mind : discoveries in cognitive science · 2026Article
- Community size rather than grammatical complexity better predicts Large Language Model accuracy in a novel Wug Test.PloS one · 2026Article
- Human-Artificial intelligence dialogue: Beyond the language barrier.Biomaterials translational · 2026Article
- Merge-based syntax is mediated by distinct neurocognitive mechanisms in 84,000 individuals with language deficits across nine languages.Scientific reports · 2025Article
- Revealing emergent human-like conceptual representations from language prediction.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- SIGNAL: Dataset for Semantic and Inferred Grammar Neurological Analysis of Language.Scientific data · 2025Article
- Comparison of Large Language Model with Aphasia.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Large language models' capabilities in responding to tuberculosis medical questions: testing ChatGPT, Gemini, and Copilot.Scientific reports · 2025Article
- What is consciousness and what it is for. An introduction to extended information theory.Frontiers in psychology · 2025Article
- Article
- Can Large Language Models Generate Useful Linguistic Corpora?: A Case Study of the Word Frequency Effect in Young German Readers.Open mind : discoveries in cognitive science · 2025Article
- Non-native speakers of English or ChatGPT: Who thinks better?F1000Research · 2025Article
- Teachers' and students' use of ChatGPT at Social science faculty in the public and private Universities of Bangladesh.F1000Research · 2025Article
- Open Science at the generative AI turn: An exploratory analysis of challenges and opportunities.Quantitative science studies · 2025Article
- Reclaiming AI as a Theoretical Tool for Cognitive Science.Computational brain & behavior · 2024Article
- A large-scale comparison of human-written versus ChatGPT-generated essays.Scientific reports · 2023Article
Corrections and comments
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5 authors.
Funding
Abstract
Large Language Models (LLMs) are recruited in applications that span from clinical assistance and legal support to question answering and education. Their success in specialized tasks has led to the claim that they possess human-like linguistic capabilities related to compositional understanding and reasoning. Yet, reverse-engineering is bound by Moravec's Paradox, according to which easy skills are hard. We systematically assess 7 state-of-the-art models on a novel benchmark. Models answered a series of comprehension questions, each prompted multiple times in two settings, permitting one-word or open-length replies. Each question targets a short text featuring high-frequency linguistic constructions. To establish a baseline for achieving human-like performance, we tested 400 humans on the same prompts. Based on a dataset of n = 26,680 datapoints, we discovered that LLMs perform at chance accuracy and waver considerably in their answers. Quantitatively, the tested models are outperformed by humans, and qualitatively their answers showcase distinctly non-human errors in language understanding. We interpret this evidence as suggesting that, despite their usefulness in various tasks, current AI models fall short of understanding language in a way that matches humans, and we argue that this may be due to their lack of a compositional operator for regulating grammatical and semantic information.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.