Results 71 to 80 of about 16,605,417 (300)

Concept Drift in Large Language Models: Challenges of Evolving Language, Contexts, and the Web

Deep learning models including large language models (LLMs) suffer from concept drift for various reasons and causes. A drift can occur either during data collection due to changes in data distribution and data imbalance, or due to external factors such ...
Thierry J. Chaussalet, Hajmohammed, M., Panagiotis Chountas, Mustafa Hajmohammed   +3 more
core   +1 more source

Large Language Models for Inorganic Synthesis Predictions [PDF]

We evaluate the effectiveness of pre-trained and fine-tuned large language models (LLMs) for predicting the synthesizability of inorganic compounds and the selection of precursors needed to perform inorganic synthesis.
Joshua, Schrier, Seongmin, Kim, Yousung, Jung   +2 more
core   +2 more sources

Idiosyncrasies in Large Language Models

CoRR
Published in ICML 2025.
Mingjie Sun, Yida Yin, Zhiqiu Xu, J. Zico Kolter, Zhuang Liu 0003   +4 more
openaire   +4 more sources

Generative AI and Large Language Models

Big Data and Cognitive Computing
In recent years, generative artificial intelligence and, in particular, large language models (LLMs) have rapidly transformed the landscape of data analysis, knowledge extraction, content generation, and intelligent decision support [...]
Fabrizio Marozzo, Riccardo Cantini
doaj   +1 more source

UiO‐66 metal–organic frameworks in biomedicine: From structural tunability to bioimaging, photodiagnostics, and photodynamic cancer therapy

FEBS Open Bio, EarlyView.
UiO‐66(Zr) metal–organic frameworks are chemically stable, biocompatible, and highly tunable nanomaterials. Their modular structure enables controlled drug delivery, multimodal bioimaging, and light‐activated photodynamic therapy, supporting integrated diagnostic and therapeutic (theranostic) applications in cancer and biomedical research.
Veronika Huntošová, Grigorii Rakhalskii, Miroslav Almáši   +2 more
wiley   +1 more source

Benchmarking large language models for extracting biobank-derived insights into health and disease

Biobank-scale datasets such as the UK Biobank have become foundational resources for advancing biomedical discovery. Yet the complexity and heterogeneity of these resources, spanning genomics, imaging, clinical records, and metadata, pose substantial ...
Manuel Corpas, Alfredo Iacoangeli
core   +1 more source

Large Language Model Unlearning

Advances in Neural Information Processing Systems 37
We study how to perform unlearning, i.e. forgetting undesirable misbehaviors, on large language models (LLMs). We show at least three scenarios of aligning LLMs with human preferences can benefit from unlearning: (1) removing harmful responses, (2) erasing copyright-protected content as requested, and (3) reducing hallucinations.
Yuanshun Yao, Xiaojun Xu, Yang Liu
openaire   +4 more sources

Large Language Models in Healthcare

CoRR
Large language models (LLMs) hold promise for transforming healthcare, from streamlining administrative and clinical workflows to enriching patient engagement and advancing clinical decision-making. However, their successful integration requires rigorous development, adaptation, and evaluation strategies tailored to clinical needs.
Mohammed Ali Al-Garadi, Tushar Mungle, Abdulaziz Ahmed, Abeed Sarker, Zhuqi Miao, Michael E. Matheny   +5 more
openaire   +4 more sources

Holding Large Language Models to Account [PDF]

, 2023
If Large Language Models can make real scientific contributions, then they can genuinely use language, be systematically wrong, and be held responsible for their errors.
Miller, Ryan
core   +1 more source

Large language models robustness against perturbation

Scientific Reports
Large Language Models (LLMs) have demonstrated impressive performance across various natural language processing (NLP) tasks, including text summarization, classification, and generation.
Saeed S. Alahmari, Lawrence Hall, Peter R. Mouton, Dmitry Goldgof   +3 more
doaj   +1 more source