核心概念
mPLUG-Owl introduces a novel training paradigm to enhance large language models with multimodal abilities through modularized learning.
要約
The content introduces mPLUG-Owl, a training paradigm for large language models that incorporates visual knowledge modules and abstractor modules to support multiple modalities. The two-stage training method aligns image and text data, showcasing impressive unimodal and multimodal abilities. Experimental results demonstrate superior performance in instruction understanding, visual comprehension, knowledge transfer, and multi-turn dialogue.
- Introduction of Large Language Models (LLMs) like GPT-3 and the need for multimodal capabilities.
- Comparison of systematic collaboration vs. end-to-end trained models for multimodal understanding.
- Presentation of mPLUG-Owl's architecture, training scheme, experimental setup, baselines comparison, quantitative analysis, ablation study, qualitative analysis.
- Evaluation on visually-related tasks using OwlEval dataset showcasing mPLUG-Owl's strengths in various abilities.
- Discussion on emerging abilities like multi-image correlation, multilingual conversation, scene text understanding.
- Limitations and further exploration areas like vision-only document comprehension and open-ended creation tasks.
統計
大規模言語モデル(LLMs)は、様々な自然言語処理(NLP)タスクで優れたパフォーマンスを示している。
GPT-3は、モデルのパラメータ数とデータサイズを拡大し、以前に見られなかったタスクでも優れたゼロショット汎用能力を示す。
mPLUG-Owlは、大規模言語モデルに視覚知識モジュールと抽象化モジュールを組み込んだトレーニングパラダイムを導入する。