Results 61 to 70 of about 106,494 (293)

On Evaluating LLM Alignment by Evaluating LLMs as Judges

CoRR
NeurIPS 2025 Camera ...
Yixin Liu, Pengfei Liu, Arman Cohan
openaire   +3 more sources

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

explain. write. edit. summarise: An Exploratory Study on Agency Negotiations in Student-Chatbot Conversations

Human-Machine Communication Journal
LLM-based chatbots such as ChatGPT have given technologies that traditionally operated on the back end a user-friendly, conversational interface. Their rapid adoption among students has prompted universities worldwide to issue guidelines and re-examine ...
Árni Már Einarsson, Ekaterina Pashevich   +1 more
doaj   +1 more source

Diffusion LLMs are Natural Adversaries for any LLM

CoRR
We introduce a novel framework that transforms the resource-intensive (adversarial) prompt optimization problem into an \emph{efficient, amortized inference task}. Our core insight is that pretrained, non-autoregressive generative LLMs, such as Diffusion LLMs, which model the joint distribution over prompt-response pairs, can serve as powerful ...
David Lüdke, Tom Wollschläger, Paul Ungermann, Stephan Günnemann, Leo Schwinn   +4 more
openaire   +2 more sources

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Constituency Parsing using LLMs [PDF]

, 2023
Constituency parsing is a fundamental yet unsolved natural language processing task. In this paper, we explore the potential of recent large language models (LLMs) that have exhibited remarkable performance across various domains and tasks to tackle this
Bai, Xuefeng, Wang, Zhongqing, Zhang, Yue, Wu, Jialong, Chen, Yulong   +4 more
core   +1 more source

An LLM-powered multi-agent system: Enabling real-time cognitive and psychological state assessment in K-12 mathematics learning

Educational Technology & Society
As artificial intelligence (AI) continues to transform education, Multi-Agent Systems (MAS) are emerging as a promising framework for delivering adaptive, personalized, and scalable learning experiences.
Fan Zhang, Wanli Xing, Chenglu Li and Wangda Zhu
doaj   +1 more source

Large Language Models for Investigating Co-Occurrences in Multi-Domain Indoor Environmental Quality [PDF]

E3S Web of Conferences
. Building occupants are continuously exposed to multiple indoor environmental stimuli, including thermal, visual, acoustic, and indoor air quality (IAQ) related factors. Prior research typically examines these multiple stimuli separately.
Li Shundong, Ma Nan
doaj   +1 more source

Who is a Better Player: LLM against LLM

CoRR
Adversarial board games, as a paradigmatic domain of strategic reasoning and intelligence, have long served as both a popular competitive activity and a benchmark for evaluating artificial intelligence (AI) systems. Building on this foundation, we propose an adversarial benchmarking framework to assess the comprehensive performance of Large Language ...
Yingjie Zhou 0003, Jiezhang Cao, Farong Wen, Li Xu 0008, Yanwei Jiang, Jun Jia, Ronghui Li, Xiaohong Liu 0001, Yu Zhou 0016, Xiongkuo Min, Jie Guo, Zicheng Zhang, Guangtao Zhai   +12 more
openaire   +2 more sources

Closed‐Loop Solid‐State Synthesis Planning for Materials Discovery With Large Language Models

Advanced Materials, EarlyView.
Leveraging literature data, we build a large‐language‐model‐driven workflow that extracts synthesis steps from 4407 papers, retrieves similar precedents, and generates candidate solid‐state synthesis recipes. The system benchmarks against ground‐truth and then operates in a closed loop with experiments to synthesize oxy‐selenide electrolyte materials ...
Dong Won Jeon, Dong Hwi Kim, Taeyang Jeon, Joo Yeong Na, Heegyu Kim, Hyunsouk Cho, Dae Soo Jung, Ju Li, Jin‐Sung Park, Sung Beom Cho   +9 more
wiley   +1 more source