The introduction of ChatGPT has led to a significant increase in the utilization of Large
Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on …

Evaluating natural language generation (NLG) is a vital but challenging problem in artificial
intelligence. Traditional evaluation metrics mainly capturing content (eg n-gram) overlap …

Large language models (LLMs) have revolutionized the field of artificial intelligence,
enabling natural language processing tasks that were previously thought to be exclusive to …

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 …

Large language models (LLMs) are increasingly deployed as the backend for various
applications, including code completion tools and AI-powered search engines. Unlike …

We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion
tokens, whose overall performance, as measured by both academic benchmarks and …

We introduce the Yi model family, a series of language and multimodal models that
demonstrate strong multi-dimensional capabilities. The Yi model family is based on 6B and …

Recent research, such as BitNet [WMD+ 23], is paving the way for a new era of 1-bit Large
Language Models (LLMs). In this work, we introduce a 1-bit LLM variant, namely BitNet b1 …

Efficient fine-tuning is vital for adapting large language models (LLMs) to downstream tasks.
However, it requires non-trivial efforts to implement these methods on different models. We …

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