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

In this paper, we present an innovative process-oriented math process reward model called
Math-shepherd, which assigns a reward score to each step of math problem solutions. The …

Large language models (LLMs) have pushed the limits of natural language understanding
and exhibited excellent problem-solving ability. Despite the great success, most existing …

Improving the alignment of language models with human preferences remains an active
research challenge. Previous approaches have primarily utilized Reinforcement Learning …

In recent years, there has been remarkable progress in leveraging Language Models (LMs),
encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models …

This survey summarises the most recent methods for building and assessing helpful, honest,
and harmless neural language models, considering small, medium, and large-size models …

Autoregressive large language models (LLMs) compress knowledge from their training data
through next-token conditional distributions. This limits tractable querying of this knowledge …

The reasoning capabilities of large language models (LLMs) play a pivotal role in the realm
of embodied artificial intelligence. Although there are effective methods like program-of …

Deploying large language models (LLMs) to real scenarios for domain-specific question
answering (QA) is a key thrust for LLM applications, which poses numerous challenges …

Starting from the resurgence of deep learning, vision-language models (VLMs) benefiting
from large language models (LLMs) have never been so popular. However, while LLMs can …