6 papers
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…
IB-GRPO: Aligning LLM-based Learning Path Recommendation with Educational Objectives via Indicator-Based Group Relative Policy Optimization
Shuai Wang, Yaoming Yang, Bingdong Li +2
Learning Path Recommendation (LPR) aims to generate personalized sequences of learning items that maximize long-term learning effect while respecting pedagogical principles and ope…
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…
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…
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…
It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization
Bingdong Li, Zixiang Di, Yanting Yang +5
In this paper, we introduce a novel approach for addressing the multi-objective optimization problem in large language model merging via black-box multi-objective optimization algo…