Recent advancements in Multimodal Large Language Models (LLMs) have focused primarily on scaling by increasing text-image pair data and enhancing LLMs to improve performance on multimodal tasks.
Ranked #1 on Visual Question Answering on MMBench (GPT-3.5 score metric)
We introduce WavCraft, a collective system that leverages large language models (LLMs) to connect diverse task-specific models for audio content creation and editing.
Compared to both open-source and proprietary models, InternVL 1. 5 shows competitive performance, achieving state-of-the-art results in 8 of 18 benchmarks.
Ranked #6 on Visual Question Answering on MM-Vet
Visual language models (VLMs) rapidly progressed with the recent success of large language models.
Ranked #24 on Visual Question Answering on MM-Vet
Proprietary LMs such as GPT-4 are often employed to assess the quality of responses from various LMs.
To achieve this objective, we present a unified self-supervised approach to learn visual representations of static-dynamic feature similarity.
While many contemporary large language models (LLMs) can process lengthy input, they still struggle to fully utilize information within the long context, known as the lost-in-the-middle challenge.
Inspired by these challenges, this paper presents AIOS, an LLM agent operating system, which embeds large language model into operating systems (OS) as the brain of the OS, enabling an operating system "with soul" -- an important step towards AGI.
Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark, reaching 99. 3% of the performance level of ChatGPT while only requiring 24 hours of finetuning on a single GPU.
We proceed to train a step-level value model designed to improve the LLM's inference process in mathematical domains.