5 citations · 10 across the 18 of their papers we have counts for
18 papers
Elmes*: Automated Construction of Fine-Grained Evaluation Rubrics for Large Language Models in Long-Tail Educational Scenarios
Tao Liu, Ye Lu, Ruohua Zhang +4
Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know. Existing benchmarks emphasize domain-general correctness or depe…
Relation Reasoning with LLMs in Expensive Optimization
Ye Lu, Bingdong Li, Aimin Zhou +1
Expensive optimization problems (EOPs) are black-box tasks with costly objective evaluations and no gradient access, making the evaluation budget the key bottleneck. Surrogate-assi…
Automating Skill Acquisition through Large-Scale Mining of Open-Source Agentic Repositories: A Framework for Multi-Agent Procedural Knowledge Extraction
Shuzhen Bi, Mengsong Wu, Hao Hao +5
The transition from monolithic large language models (LLMs) to modular, skill-equipped agents represents a fundamental architectural shift in artificial intelligence deployment. Wh…
Scaling Laws for Educational AI Agents
Mengsong Wu, Hao Hao, Shuzhen Bi +5
While scaling laws for Large Language Models (LLMs) have been extensively studied along dimensions of model parameters, training data, and compute, the scaling behavior of LLM-base…
See and Remember: A Multimodal Agent for Web Traversal
Xinjun Wang, Shengyao Wang, Aimin Zhou +1
Autonomous web navigation requires agents to perceive complex visual environments and maintain long-term context, yet current Large Language Model (LLM) based agents often struggle…
AutoSynth: Automated Workflow Optimization for High-Quality Synthetic Dataset Generation via Monte Carlo Tree Search
Shuzhen Bi, Chang Song, Siyu Song +5
Supervised fine-tuning (SFT) of large language models (LLMs) for specialized tasks requires high-quality datasets, but manual curation is prohibitively expensive. Synthetic data ge…
EA4LLM: A Gradient-Free Approach to Large Language Model Optimization via Evolutionary Algorithms
WenTao Liu, Siyu Song, Hao Hao +1
In recent years, large language models (LLMs) have made remarkable progress, with model optimization primarily relying on gradient-based optimizers such as Adam. However, these gra…
SID: Benchmarking Guided Instruction Capabilities in STEM Education with a Socratic Interdisciplinary Dialogues Dataset
Mei Jiang, Houping Yue, Bingdong Li +4
Fostering students' abilities for knowledge integration and transfer in complex problem-solving scenarios is a core objective of modern education, and interdisciplinary STEM is a k…
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…
EduAlign: Aligning Educational LLMs for Helpfulness, Personalization, and Creativity
Siyu Song, Wentao Liu, Ye Lu +8
Educational language models should pursue three complementary objectives: helpfulness through responsible guidance, personalization to learner needs, and creativity that supports e…
A Large Language Model for Chemistry and Retrosynthesis Predictions
Yueqing Zhang, Wentao Liu, Yan Zhang +9
Large language models (LLM) have achieved impressive progress across a broad range of general-purpose tasks, but their effectiveness in chemistry remains limited due to scarce doma…
Echo: A Large Language Model with Temporal Episodic Memory
WenTao Liu, Ruohua Zhang, Aimin Zhou +2
Research on large language models (LLMs) has shown remarkable performance in domains such as mathematics, programming, and literary creation. However, most studies have focused on…
Un-evaluated Solutions May Be Valuable in Expensive Optimization
Hao Hao, Xiaoqun Zhang, Aimin Zhou
Expensive optimization problems (EOPs) are prevalent in real-world applications, where the evaluation of a single solution requires a significant amount of resources. In our study…
Large Language Models as Surrogate Models in Evolutionary Algorithms: A Preliminary Study
Hao Hao, Xiaoqun Zhang, Aimin Zhou
Large Language Models (LLMs) have achieved significant progress across various fields and have exhibited strong potential in evolutionary computation, such as generating new soluti…
A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms
Hao Hao, Xiaoqun Zhang, Bingdong Li +1
Surrogate-assisted Evolutionary Algorithm (SAEA) is an essential method for solving expensive expensive problems. Utilizing surrogate models to substitute the optimization function…
Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis
Hao Hao, Xiaoqun Zhang, Aimin Zhou
Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings. This…
Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization
Hao Hao, Xiaoqun Zhang, Aimin Zhou
Surrogate-assisted evolutionary algorithms (SAEAs) hold significant importance in resolving expensive optimization problems~(EOPs). Extensive efforts have been devoted to improving…
Evolutionary Retrosynthetic Route Planning
Yan Zhang, Hao Hao, Xiao He +2
Molecular retrosynthesis is a significant and complex problem in the field of chemistry, however, traditional manual synthesis methods not only need well-trained experts but also a…