Abstract Recent advances in Large Language Models (LLMs) have achieved remarkable
breakthroughs in understanding and responding to user intents. However, their performance …

Large Language Models (LLMs), built upon Transformer-based architectures with massive
pretraining on diverse data, have not only revolutionized natural language processing but …

Large Language Models (LLMs) have excelled as high-level semantic planners for
sequential decision-making tasks. However, harnessing them to learn complex low-level …

Finetuning large language models (LLMs) on instructions leads to vast performance
improvements on natural language tasks. We apply instruction tuning using code …

Given the recent impressive accomplishments of language models (LMs) for code
generation, we explore the use of LMs as general adaptive mutation and crossover …

Evolutionary algorithms (EAs) have achieved remarkable success in tackling complex
combinatorial optimization problems. However, EAs often demand carefully-designed …

In this big data era, the use of large dataset in conjunction with machine learning (ML) has
been increasingly popular in both industry and academia. In recent times, the field of …

The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain
experts to engage in trial-and-error heuristic design. The long-standing endeavor of design …

Large language models (LLMs) have emerged as powerful tools capable of accomplishing a
broad spectrum of tasks. Their abilities span numerous areas, and one area where they …

Large Transformer models are capable of implementing a plethora of so-called in-context
learning algorithms. These include gradient descent, classification, sequence completion …