Results 131 to 140 of about 106,494 (293)

Structural Divergence Between the Moltbook AI‐Agent Network and Human Social Networks

Advanced Science, EarlyView.
Analysis of the Moltbook AI‐agent network reveals a striking combination of familiar global scaling and distinct internal organization. Attention is highly concentrated, reciprocity is limited, connected triads are suppressed, and communities are strongly modular.
Wenpin Hou, Zhicheng Ji
wiley   +1 more source

Can Editing LLMs Inject Harm? [PDF]

Knowledge editing has been increasingly adopted to correct the false or outdated knowledge in Large Language Models (LLMs). Meanwhile, one critical but under-explored question is: can knowledge editing be used to inject harm into LLMs?
Xu, Xiongxiao, Huang, Baixiang, Xiao, Chaowei, Wang, William Yang, Gu, Jia-Chen, Lai, Shiyang, Song, Dawn, Gu, Jindong, Yao, Huaxiu, Shu, Kai, Li, Zekun, Chen, Canyu, Chen, Zhaorun, Yan, Xifeng, Torr, Philip   +14 more
core   +1 more source

Evaluating Multiple Input Strategies of Large Language Models for Gallbladder Polyps on Ultrasound: Comparative Study

JMIR Medical Informatics
BackgroundGallbladder polyps have a high prevalence and are predominantly benign lesions, often detected via ultrasound. They impose diagnostic burdens on radiologists while generating substantial patient demand for report ...
Lin Jiang, Jiaqian Yao, Zebang Yang, Fuqiu Tang, Xin Zheng, Xiaoer Zhang, Xiaoyan Xie, Ming Xu, Tongyi Huang   +8 more
doaj   +1 more source

The Illusion of Explainability with LLMs and LLM-Agents

The formal linguistic capabilities of Large Language Models (LLMs) are increasingly intersecting with the field of Explainable Agency (XAg). The growing adoption of LLM-agents has heightened the need to explain their behaviour to users. However, current methods consistently fail to meet desirable properties of human-centric explanations.
Montese, Sara, Alvarez-Napagao, Sergio, Gimenez-Abalos, Victor, Cortés, Ulises   +3 more
openaire   +2 more sources

LLM-Evaluation Tropes: Perspectives on the Validity of LLM-Evaluations

CoRR
Large Language Models (LLMs) are increasingly used to evaluate information retrieval (IR) systems, generating relevance judgments traditionally made by human assessors. Recent empirical studies suggest that LLM-based evaluations often align with human judgments, leading some to suggest that human judges may no longer be necessary, while others ...
Laura Dietz, Oleg Zendel, Peter Bailey, Charles L. A. Clarke, Ellese Cotterill, Jeff Dalton 0001, Faegheh Hasibi, Mark Sanderson, Nick Craswell   +8 more
openaire   +3 more sources

MAPA: A Semantic Network Framework for Functional Module Discovery and Interpretation in Multi‐Omics Data

Advanced Science, EarlyView.
MAPA transforms complex multi‐omics data into biologically coherent functional modules by integrating pathway information with molecular interaction networks. Retrieval‐augmented large language models then generate structured, literature‐informed interpretations.
Yifei Ge, Feifan Zhang, Yijiang Liu, Chao Jiang, Peng Gao, Nguan Soon Tan, Sai Zhang, Yuchen Shen, Qianyi Zhou, Xin Zhou, Xiao Wang, Fangqing Zhao, Chuchu Wang, Xiaotao Shen   +13 more
wiley   +1 more source

Enthalten LLMs Wissen (über irgendwas)?

This article investigates whether Large Language Models (LLMs), a subset of Machine Learning (ML), can be considered to process theoretical knowledge. LLMs are ML models trained on large linguistic, textual datasets.
Pégny, Maël
core   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

A Practical Guide for Evaluating LLMs and LLM-Reliant Systems

CoRR
Recent advances in generative AI have led to remarkable interest in using systems that rely on large language models (LLMs) for practical applications. However, meaningful evaluation of these systems in real-world scenarios comes with a distinct set of challenges, which are not well-addressed by synthetic benchmarks and de-facto metrics that are often ...
Ethan M. Rudd, Christopher Andrews, Philip Tully   +2 more
openaire   +3 more sources

Determinants of Knowledge and Usage of Generative Artificial Intelligence in Agricultural Extension: Evidence From Tennessee Extension Personnel

Agribusiness, EarlyView.
ABSTRACT This paper examines the determinants of generative AI (GenAI) knowledge and usage among agricultural extension professionals. Drawing on survey data from agricultural extension personnel in Tennessee, we employ regression analyses and latent Dirichlet allocation (LDA) for topic modeling of open‐ended responses to study the knowledge and usage ...
Abdelaziz Lawani, Jason de Koff, James Muriuki, Anna‐Liisa Ihuhwa   +3 more
wiley   +1 more source