Misinformation such as fake news and rumors is a serious threat on information ecosystems
and public trust. The emergence of Large Language Models (LLMs) has great potential to …

With the development of large language models (LLMs), striking a balance between the
performance and safety of AI systems has never been more critical. However, the inherent …

In coming decades, artificial general intelligence (AGI) may surpass human capabilities at
many critical tasks. We argue that, without substantial effort to prevent it, AGIs could learn to …

This work identifies 18 foundational challenges in assuring the alignment and safety of large
language models (LLMs). These challenges are organized into three different categories …

Reinforcement learning from human feedback (RLHF) is a variant of reinforcement learning
(RL) that learns from human feedback instead of relying on an engineered reward function …

Autonomous interaction with the computer has been a longstanding challenge with great
potential, and the recent proliferation of large language models (LLMs) has markedly …

Efforts to align Large Language Models (LLMs) are mainly conducted via Reinforcement
Learning from Human Feedback (RLHF) methods. However, RLHF encounters major …

Large language models (LLMs) are reported to be partial to certain cultures owing to the
training data dominance from the English corpora. Since multilingual cultural data are often …

In the rapidly evolving field of machine learning (ML), data augmentation (DA) has emerged
as a pivotal technique for enhancing model performance by diversifying training examples …

Large Language Model (LLM) agents have been increasingly adopted as simulation tools to
model humans in applications such as social science. However, one fundamental question …