AI alignment aims to make AI systems behave in line with human intentions and values. As
AI systems grow more capable, so do risks from misalignment. To provide a comprehensive …

Large language models (LLMs) have demonstrated remarkable performance across a wide
array of NLP tasks. However, their efficacy is undermined by undesired and inconsistent …

In this paper, we introduce the BeaverTails dataset, aimed at fostering research on safety
alignment in large language models (LLMs). This dataset uniquely separates annotations of …

Despite their remarkable capabilities, large language models (LLMs) often produce
responses containing factual inaccuracies due to their sole reliance on the parametric …

Reinforcement learning from human feedback (RLHF) is a technique for training AI systems
to align with human goals. RLHF has emerged as the central method used to finetune state …

Generation of plausible yet incorrect factual information, termed hallucination, is an
unsolved issue in large language models. We study the ability of language models to …

Abstract Multimodal Large Language Models (MLLMs) have recently demonstrated
impressive capabilities in multimodal understanding reasoning and interaction. However …

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 …

Instruction tuned Large Vision Language Models (LVLMs) have significantly advanced in
generalizing across a diverse set of multi-modal tasks, especially for Visual Question …

Large language models (LLMs) often contain misleading content, emphasizing the need to
align them with human values to ensure secure AI systems. Reinforcement learning from …