Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

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 …

The rapid rise in popularity of Large Language Models (LLMs) with emerging capabilities
has spurred public curiosity to evaluate and compare different LLMs, leading many …

Large language models (LLMs) offer impressive performance in various zero-shot and few-
shot tasks. However, their success in zero-shot or few-shot settings may be affected by task …

With the rise of Large Language Models (LLMs) in recent years, new opportunities are
emerging, but also new challenges, and contamination is quickly becoming critical …

We introduce SOLAR 10.7 B, a large language model (LLM) with 10.7 billion parameters,
demonstrating superior performance in various natural language processing (NLP) tasks …

We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-
centric approach. While stronger language models can enhance multimodal capabilities, the …

Despite making significant progress in multi-modal tasks, current Multi-modal Large
Language Models (MLLMs) encounter the significant challenge of hallucination, which may …

In-context learning is a promising paradigm that utilizes in-context examples as prompts for
the predictions of large language models. These prompts are crucial for achieving strong …

The evaluation of large language models (LLMs) is crucial to assess their performance and
mitigate potential security risks. In this paper, we introduce PromptBench, a unified library to …