Results 131 to 140 of about 56,050 (312)

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Improving Retrieval-Augmented Generation Performance Using the MAF-RAG Architecture, EVR–VOR Vector Retrieval, and Multi-Agent Fallback Reasoning

Journal of Applied Informatics and Computing
Retrieval-Augmented Generation (RAG) AI chatbots have gained popularity for their effectiveness in producing accurate, fast, and reliable responses; however, they have faced critical challenges stemming from limited datasets, outdated documents, and ...
Erlanda Prasetio, L. Budi Handoko Handoko, Khafiiz Hastuti   +2 more
doaj   +1 more source

Scheming Ability in LLM-to-LLM Strategic Interactions

CoRR
As large language model (LLM) agents are deployed autonomously in diverse contexts, evaluating their capacity for strategic deception becomes crucial. While recent research has examined how AI systems scheme against human developers, LLM-to-LLM scheming remains underexplored.
openaire   +2 more sources

Safe-Child-LLM: A Developmental Benchmark for Evaluating LLM Safety in Child-LLM Interactions

CoRR
As Large Language Models (LLMs) increasingly power applications used by children and adolescents, ensuring safe and age-appropriate interactions has become an urgent ethical imperative. Despite progress in AI safety, current evaluations predominantly focus on adults, neglecting the unique vulnerabilities of minors engaging with generative AI.
Junfeng Jiao, Saleh Afroogh, Kevin Chen, Abhejay Murali, David Atkinson, Amit Dhurandhar   +5 more
openaire   +2 more sources

LLM Assistants for Interaction with Autonomous Systems

reservedIl presente lavoro di tesi descrive la progettazione e l’implementazione di un sistema per l’addestramento e la gestione di modelli linguistici di larga scala (LLM), capaci di adattarsi automaticamente alle risorse hardware disponibili e operare ...
ZINGRILLARA, MATTEO
core  

LLM Students 1995-96

, 1995
(Left to right) Back row: Ashley Shannauer, Steve Wolff, Lisa Kirby, Daniel Belcourt, and Lucinda Lomas.

core  

Perceived trustworthiness of LLM

Large language models (LLMs) are increasingly part of everyday life, yet there is no established way to measure how users evaluate their trustworthiness.
Ala Yankouskaya, Basad Barajeeh, HO, Chun Sing Maxwell, Raian Ali, Areej Babiker, MA, Yunsi Tina [LTTC], Sameha Alshakhsi   +6 more
core   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

SKR1: Benchmark for Testing Knowledge About Slovak Realia for Large Language Models

Acta Informatica Pragensia
Background: To objectively evaluate the capabilities of large language models (LLMs), we need to develop tools that enable such assessment. While numerous benchmarks exist, the vast majority are in English and focus on general knowledge, often ...
Marek Dobeš
doaj   +1 more source

GUARD-D-LLM: An LLM-Based Risk Assessment Engine for the Downstream uses of LLMs

CoRR
Amidst escalating concerns about the detriments inflicted by AI systems, risk management assumes paramount importance, notably for high-risk applications as demanded by the European Union AI Act. Guidelines provided by ISO and NIST aim to govern AI risk management; however, practical implementations remain scarce in scholarly works.
Sundaraparipurnan Narayanan, Sandeep Vishwakarma   +1 more
openaire   +2 more sources