Abstract Large Language Models (LLMs), such as ChatGPT and Bard, have revolutionized
natural language understanding and generation. They possess deep language …

Large Language Models (LLMs) have recently demonstrated remarkable capabilities in
natural language processing tasks and beyond. This success of LLMs has led to a large …

Ever since the Turing Test was proposed in the 1950s, humans have explored the mastering
of language intelligence by machine. Language is essentially a complex, intricate system of …

Training large language models (LLMs) with open-domain instruction following data brings
colossal success. However, manually creating such instruction data is very time-consuming …

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 …

Large language models (LLMs), such as GPT-4, have shown remarkable performance in
natural language processing (NLP) tasks, including challenging mathematical reasoning …

Mathematical reasoning poses a significant challenge for language models due to its
complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which …

Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …

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 …

Large Language Models (LLMs) trained on extensive textual corpora have emerged as
leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite …