A major factor in the recent success of large language models is the use of enormous and
ever-growing text datasets for unsupervised pre-training. However, naively training a model …

Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-
learning, or few-shot learning, aims to effectively train a model using only a small amount of …

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 …

Large Language Models (LLMs) have demonstrated remarkable capabilities in important
tasks such as natural language understanding and language generation, and thus have the …

Abstract Mixture-of-Experts (MoE) has gained increasing popularity as a promising
framework for scaling up large language models (LLMs). However, training MoE from …

Instruction tuning has unlocked powerful capabilities in large language models (LLMs),
effectively using combined datasets to develop generalpurpose chatbots. However, real …

Multimodal large language models (MLLMs) enhance the capabilities of standard large
language models by integrating and processing data from multiple modalities, including text …

Solving mathematical problems requires advanced reasoning abilities and presents notable
challenges for large language models. Previous works usually synthesize data from …

With contributions from the open-source community, a vast amount of instruction tuning (IT)
data has emerged. Given the significant resource allocation required for training and …

Table reasoning aims to generate inference results based on the user requirement and the
provided table. Enhancing the table reasoning capability of the model can aid in obtaining …