The integration of Large Language Models (LLMs) into various domains is driving significant advancements in automation, optimization, and innovation. In the realm of algorithm design, LLMs are enhancing problem-solving capabilities across optimization, machine learning, and scientific exploration. In high-performance computing, LLMs are optimizing parameter tuning for virtual screening applications, improving efficiency and accuracy. The field of procedural content generation in games is also seeing a transformative shift with LLMs, enabling more sophisticated and engaging content creation. Additionally, LLMs are making inroads into education, particularly in computer science, by assisting with code generation, debugging, and personalized learning experiences. The synergy between LLMs and model-driven engineering is another promising area, with LLMs aiding in tasks such as model repository classification and recommender systems development. Overall, the trend is towards leveraging LLMs to automate complex processes, optimize performance, and enhance human-computer interactions across diverse fields.
Leveraging LLMs for Automation and Optimization Across Disciplines
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A Systematic Survey on Large Language Models for Algorithm Design
Efficient Parameter Tuning for a Structure-Based Virtual Screening HPC Application
SPRIG: Improving Large Language Model Performance by System Prompt Optimization
AutoFPDesigner: Automated Flight Procedure Design Based on Multi-Agent Large Language Model
Optimizing Large Language Models for Dynamic Constraints through Human-in-the-Loop Discriminators
Procedural Content Generation in Games: A Survey with Insights on Emerging LLM Integration
In-the-loop Hyper-Parameter Optimization for LLM-Based Automated Design of Heuristics
Large Language Models in Computer Science Education: A Systematic Literature Review
LLM-based Optimization of Compound AI Systems: A Survey
SPHERE: Scaling Personalized Feedback in Programming Classrooms with Structured Review of LLM Outputs
PromptHive: Bringing Subject Matter Experts Back to the Forefront with Collaborative Prompt Engineering for Educational Content Creation
Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent
Hybrid Generative AI for De Novo Design of Co-Crystals with Enhanced Tabletability
Deep Memory Search: A Metaheuristic Approach for Optimizing Heuristic Search
On the use of Large Language Models in Model-Driven Engineering
In Context Learning and Reasoning for Symbolic Regression with Large Language Models
Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Design