Large language models eroding science understanding: an experimental study
Fuente:
arXiv
Saved in:
| Main Authors: | Collins, Harry, Grote, Hartmut, Newbury, Paul, Sutton, Patrick, Thorne, Simon |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Large Language Models and Scientific Discourse: Where's the Intelligence?
by: Collins, Harry, et al.
Published: (2026)
by: Collins, Harry, et al.
Published: (2026)
Large language models in medicine: the potentials and pitfalls
by: Omiye, Jesutofunmi A., et al.
Published: (2023)
by: Omiye, Jesutofunmi A., et al.
Published: (2023)
Large language models accurately predict public perceptions of support for climate action worldwide
by: Powdthavee, Nattavudh, et al.
Published: (2026)
by: Powdthavee, Nattavudh, et al.
Published: (2026)
Fact-checking information from large language models can decrease headline discernment
by: DeVerna, Matthew R., et al.
Published: (2023)
by: DeVerna, Matthew R., et al.
Published: (2023)
Large language models can consistently generate high-quality content for election disinformation operations
by: Williams, Angus R., et al.
Published: (2024)
by: Williams, Angus R., et al.
Published: (2024)
Uncovering inequalities in new knowledge learning by large language models across different languages
by: Wang, Chenglong, et al.
Published: (2025)
by: Wang, Chenglong, et al.
Published: (2025)
AI-generated data contamination erodes pathological variability and diagnostic reliability
by: He, Hongyu, et al.
Published: (2026)
by: He, Hongyu, et al.
Published: (2026)
Failure of contextual invariance in large language models
by: Kumar, Sagar, et al.
Published: (2026)
by: Kumar, Sagar, et al.
Published: (2026)
Retrieval-augmented reasoning with lean language models
by: Chan, Ryan Sze-Yin, et al.
Published: (2025)
by: Chan, Ryan Sze-Yin, et al.
Published: (2025)
Do Chinese models speak Chinese languages?
by: Wen-Yi, Andrea W, et al.
Published: (2025)
by: Wen-Yi, Andrea W, et al.
Published: (2025)
First, do NOHARM: towards clinically safe large language models
by: Wu, David, et al.
Published: (2025)
by: Wu, David, et al.
Published: (2025)
Emergent evaluation hubs in a decentralizing large language model ecosystem
by: Cebrian, Manuel, et al.
Published: (2025)
by: Cebrian, Manuel, et al.
Published: (2025)
PsychBench: Auditing Epidemiological Fidelity in Large Language Model Mental Health Simulations
by: Keough, Patrick
Published: (2026)
by: Keough, Patrick
Published: (2026)
The threat of analytic flexibility in using large language models to simulate human data
by: Cummins, Jamie
Published: (2025)
by: Cummins, Jamie
Published: (2025)
Testing GPT-4-o1-preview on math and science problems: A follow-up study
by: Davis, Ernest
Published: (2024)
by: Davis, Ernest
Published: (2024)
Large language models for spreading dynamics in complex systems
by: Jiang, Shuyu, et al.
Published: (2026)
by: Jiang, Shuyu, et al.
Published: (2026)
Simulating multiple human perspectives in socio-ecological systems using large language models
by: Zeng, Yongchao, et al.
Published: (2025)
by: Zeng, Yongchao, et al.
Published: (2025)
Potential of large language model-powered nudges for promoting daily water and energy conservation
by: Li, Zonghan, et al.
Published: (2025)
by: Li, Zonghan, et al.
Published: (2025)
What can large language models do for sustainable food?
by: Thomas, Anna T., et al.
Published: (2025)
by: Thomas, Anna T., et al.
Published: (2025)
AI Propaganda factories with language models
by: Olejnik, Lukasz
Published: (2025)
by: Olejnik, Lukasz
Published: (2025)
Automating psychological hypothesis generation with AI: when large language models meet causal graph
by: Tong, Song, et al.
Published: (2024)
by: Tong, Song, et al.
Published: (2024)
When simulations look right but causal effects go wrong: Large language models as behavioral simulators
by: Li, Zonghan, et al.
Published: (2026)
by: Li, Zonghan, et al.
Published: (2026)
Using AI Alignment Theory to understand the potential pitfalls of regulatory frameworks
by: Tlaie, Alejandro
Published: (2024)
by: Tlaie, Alejandro
Published: (2024)
Developmental trajectories of decision making and affective dynamics in large language models
by: Wang, Zhihao, et al.
Published: (2025)
by: Wang, Zhihao, et al.
Published: (2025)
AI-AI Bias: large language models favor communications generated by large language models
by: Laurito, Walter, et al.
Published: (2024)
by: Laurito, Walter, et al.
Published: (2024)
Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals
by: Roberson, Elisha D. O.
Published: (2025)
by: Roberson, Elisha D. O.
Published: (2025)
ELEPHANT: Measuring and understanding social sycophancy in LLMs
by: Cheng, Myra, et al.
Published: (2025)
by: Cheng, Myra, et al.
Published: (2025)
Hallucination, reliability, and the role of generative AI in science
by: Rathkopf, Charles
Published: (2025)
by: Rathkopf, Charles
Published: (2025)
AI in data science education: experiences from the classroom
by: Hageman, J. A., et al.
Published: (2025)
by: Hageman, J. A., et al.
Published: (2025)
Enhanced Interpretable Knowledge Tracing for Students Performance Prediction with Human understandable Feature Space
by: Minn, Sein, et al.
Published: (2025)
by: Minn, Sein, et al.
Published: (2025)
Evidence of a log scaling law for political persuasion with large language models
by: Hackenburg, Kobi, et al.
Published: (2024)
by: Hackenburg, Kobi, et al.
Published: (2024)
Training language models to be warm and empathetic makes them less reliable and more sycophantic
by: Ibrahim, Lujain, et al.
Published: (2025)
by: Ibrahim, Lujain, et al.
Published: (2025)
A closer look at how large language models trust humans: patterns and biases
by: Lerman, Valeria, et al.
Published: (2025)
by: Lerman, Valeria, et al.
Published: (2025)
A survey on fairness of large language models in e-commerce: progress, application, and challenge
by: Ren, Qingyang, et al.
Published: (2024)
by: Ren, Qingyang, et al.
Published: (2024)
Measuring Research Convergence in Interdisciplinary Teams Using Large Language Models and Graph Analytics
by: Li, Wenwen, et al.
Published: (2026)
by: Li, Wenwen, et al.
Published: (2026)
Artificially intelligent agents in the social and behavioral sciences: A history and outlook
by: Holme, Petter, et al.
Published: (2025)
by: Holme, Petter, et al.
Published: (2025)
Towards understanding evolution of science through language model series
by: Dong, Junjie, et al.
Published: (2024)
by: Dong, Junjie, et al.
Published: (2024)
Safety challenges of AI in medicine in the era of large language models
by: Wang, Xiaoye, et al.
Published: (2024)
by: Wang, Xiaoye, et al.
Published: (2024)
Street-Level AI: Are Large Language Models Ready for Real-World Judgments?
by: Pokharel, Gaurab, et al.
Published: (2025)
by: Pokharel, Gaurab, et al.
Published: (2025)
Large Language Models Can Be a Viable Substitute for Expert Political Surveys When a Shock Disrupts Traditional Measurement Approaches
by: Wu, Patrick Y.
Published: (2025)
by: Wu, Patrick Y.
Published: (2025)
Similar Items
-
Large Language Models and Scientific Discourse: Where's the Intelligence?
by: Collins, Harry, et al.
Published: (2026) -
Large language models in medicine: the potentials and pitfalls
by: Omiye, Jesutofunmi A., et al.
Published: (2023) -
Large language models accurately predict public perceptions of support for climate action worldwide
by: Powdthavee, Nattavudh, et al.
Published: (2026) -
Fact-checking information from large language models can decrease headline discernment
by: DeVerna, Matthew R., et al.
Published: (2023) -
Large language models can consistently generate high-quality content for election disinformation operations
by: Williams, Angus R., et al.
Published: (2024)