7 citations · 17 across the 7 of their papers we have counts for
7 papers
Towards Agentic Intelligence for Materials Science
Huan Zhang, Yizhan Li, Wenhao Huang +18
The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true acceleration in discovery requires moving beyond task-iso…
EduResearchBench: A Hierarchical Atomic Task Decomposition Benchmark for Full-Lifecycle Educational Research
Houping Yue, Zixiang Di, Mei Jiang +5
While Large Language Models (LLMs) are reshaping the paradigm of AI for Social Science (AI4SS), rigorously evaluating their capabilities in scholarly writing remains a major challe…
SMRC: Aligning Large Language Models with Student Reasoning for Mathematical Error Correction
Biaojie Zeng, Min Zhang, Juan Zhou +4
Large language models (LLMs) often make reasoning errors when solving mathematical problems, and how to automatically detect and correct these errors has become an important resear…
ELMES: An Automated Framework for Evaluating Large Language Models in Educational Scenarios
Shou'ang Wei, Xinyun Wang, Shuzhen Bi +9
The emergence of Large Language Models (LLMs) presents transformative opportunities for education, generating numerous novel application scenarios. However, significant challenges…
HoneyComb: A Flexible LLM-Based Agent System for Materials Science
Huan Zhang, Yu Song, Ziyu Hou +2
The emergence of specialized large language models (LLMs) has shown promise in addressing complex tasks for materials science. Many LLMs, however, often struggle with distinct comp…
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science
Yu Song, Santiago Miret, Huan Zhang +1
We propose an instruction-based process for trustworthy data curation in materials science (MatSci-Instruct), which we then apply to finetune a LLaMa-based language model targeted…
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling
Yu Song, Santiago Miret, Bang Liu
We present MatSci-NLP, a natural language benchmark for evaluating the performance of natural language processing (NLP) models on materials science text. We construct the benchmark…