Instruction tuning of BLIP-2 with an instruction-aware Query Transformer delivers state-of-the-art zero-shot performance on held-out vision-language datasets and strong finetuned results on downstream tasks.
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Qinyuan Ye, Bill Yuchen Lin, and Xiang Ren
Canonical reference. 80% of citing Pith papers cite this work as background.
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80% of classified citations
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2023 6representative citing papers
MME is a manually annotated benchmark evaluating MLLMs on perception and cognition across 14 subtasks to avoid data leakage and support fair model comparisons.
Otter is a multi-modal model instruction-tuned on the MIMIC-IT dataset of over 3 million in-context instruction-response pairs to improve convergence and generalization on tasks with multiple images and videos.
The Flan Collection demonstrates that task balancing, data enrichment, and mixed prompt training are critical to effective instruction tuning, yielding stronger Flan-T5 models released publicly.
This survey organizes the architectures, training strategies, data, evaluation methods, extensions, and challenges of Multimodal Large Language Models.
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.
citing papers explorer
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InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Instruction tuning of BLIP-2 with an instruction-aware Query Transformer delivers state-of-the-art zero-shot performance on held-out vision-language datasets and strong finetuned results on downstream tasks.
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MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
MME is a manually annotated benchmark evaluating MLLMs on perception and cognition across 14 subtasks to avoid data leakage and support fair model comparisons.
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Otter: A Multi-Modal Model with In-Context Instruction Tuning
Otter is a multi-modal model instruction-tuned on the MIMIC-IT dataset of over 3 million in-context instruction-response pairs to improve convergence and generalization on tasks with multiple images and videos.
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The Flan Collection: Designing Data and Methods for Effective Instruction Tuning
The Flan Collection demonstrates that task balancing, data enrichment, and mixed prompt training are critical to effective instruction tuning, yielding stronger Flan-T5 models released publicly.
-
A Survey on Multimodal Large Language Models
This survey organizes the architectures, training strategies, data, evaluation methods, extensions, and challenges of Multimodal Large Language Models.
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A Comprehensive Overview of Large Language Models
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.