LLaVa : Improved Baselines with Visual Instruction Tuning
Date : 2023-10-05
Description
LLaVA represents a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding, achieving impressive chat capabilities mimicking spirits of the multimodal GPT-4 and setting a new state-of-the-art accuracy on Science QA.
Project page (below) links to research paper, GitHub repo, demo and dataset.
Recently on :
Artificial Intelligence
Information Processing | Computing
Research
WEB - 2025-11-13
Measuring political bias in Claude
Anthropic gives insights into their evaluation methods to measure political bias in models.
WEB - 2025-10-09
Defining and evaluating political bias in LLMs
OpenAI created a political bias evaluation that mirrors real-world usage to stress-test their models’ ability to remain objecti...
WEB - 2025-07-23
Preventing Woke AI In Federal Government
Citing concerns that ideological agendas like Diversity, Equity, and Inclusion (DEI) are compromising accuracy, this executive ...
WEB - 2025-07-10
America’s AI Action Plan
To win the global race for technological dominance, the US outlined a bold national strategy for unleashing innovation, buildin...
WEB - 2024-12-30
Fine-tune ModernBERT for text classification using synthetic data
David Berenstein explains how to finetune a ModernBERT model for text classification on a synthetic dataset generated from argi...