Instruction tuning large language models (LLMs) using machine-generated instruction-
following data has been shown to improve zero-shot capabilities on new tasks, but the idea …

High-quality instructions and responses are essential for the zero-shot performance of large
language models on interactive natural language tasks. For interactive vision-language …

Large-scale pre-training and instruction tuning have been successful at creating general-
purpose language models with broad competence. However, building general-purpose …

Visual language models (VLMs) rapidly progressed with the recent success of large
language models. There have been growing efforts on visual instruction tuning to extend the …

Instruction tuning of the Large Vision-language Models (LVLMs) has revolutionized the
development of versatile models with zero-shot generalization across a wide range of …

How to efficiently transform large language models (LLMs) into instruction followers is
recently a popular research direction, while training LLM for multi-modal reasoning remains …

Instruction tuning, a new learning paradigm that fine-tunes pre-trained language models on
tasks specified through instructions, has shown promising zero-shot performance on various …

We introduce VisIT-Bench (Visual InsTruction Benchmark), a benchmark for evaluation of
instruction-following vision-language models for real-world use. Our starting point is curating …

Multimodal large language models acquire their instruction-following capabilities through a
two-stage training process: pre-training on image-text pairs and fine-tuning on supervised …

Large language models (LLMs) have demonstrated excellent zero-shot generalization to
new language tasks. However, effective utilization of LLMs for zero-shot visual question …