Large language models (LLMs) are gaining increasing popularity in both academia and
industry, owing to their unprecedented performance in various applications. As LLMs …

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 …

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

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 …

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 …

The rapid development of open-source large language models (LLMs) has been truly
remarkable. However, the scaling law described in previous literature presents varying …

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 …

In the era of large language models, Mixture-of-Experts (MoE) is a promising architecture for
managing computational costs when scaling up model parameters. However, conventional …

This paper embarks on an exploration into the Large Language Model (LLM) datasets,
which play a crucial role in the remarkable advancements of LLMs. The datasets serve as …

As Large Language Models (LLMs) continue to advance in performance, their size has
escalated significantly, with current LLMs containing billions or even trillions of parameters …