Large language models (LLMs) have shown powerful performance and development
prospect and are widely deployed in the real world. However, LLMs can capture social …

Abstract Foundation and Large Language Models (FLLMs) are models that are trained using
a massive amount of data with the intent to perform a variety of downstream tasks. FLLMs …

There has been a recent explosion of research on Large Language Models (LLMs) for
software engineering tasks, in particular code generation. However, results from LLMs can …

Research in both ecology and AI strives for predictive understanding of complex systems,
where nonlinearities arise from multidimensional interactions and feedbacks across multiple …

In everyday life, humans often plan their actions by following step-by-step instructions in the
form of goal-oriented scripts. Previous work has exploited language models (LMs) to plan for …

There has been a recent explosion of research on Large Language Models (LLMs) for
software engineering tasks, in particular code generation. However, results from LLMs can …

Machine Unlearning (MU) has recently gained considerable attention due to its potential to
achieve Safe AI by removing the influence of specific data from trained Machine Learning …

Statistical measures for group fairness in machine learning reflect the gap in performance of
algorithms across different groups. These measures, however, exhibit a high variance …

The rapid advancement of large language models (LLMs) has revolutionized artificial
intelligence, introducing unprecedented capabilities in natural language processing and …

Mitigating algorithmic bias is a critical task in the development and deployment of machine
learning models. While several toolkits exist to aid machine learning practitioners in …