November 27, 2024

Table of Links Abstract and 1 Introduction 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 6 Related Works 7 Conclusions and References A

Table of Links Abstract and 1 Introduction 2 Background & Problem Statement 2.1 How can we use MLLMs for Diffusion Synthesis that Synergizes both sides? 3 DreamLLM 3.1 End-to-End Interleaved generative Pretraining (I-GPT) 3.2 Model Training 4 Experiments and 4.1 Multimodal Comprehension 4.2 Text-Conditional Image Synthesis 4.3 Multimodal Joint Creation & Comprehension 5 Discussions 5.1 Synergy between creation & Comprehension? 5. 2 What is learned by DreamLLM? 6 Related Works 7 Conclusions and References A

Authors: (1) Limeng Zhang, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia; (2) M. Ali Babar, Centre for Research on Engineering Software Technologies (CREST), The University of Adelaide, Australia. Table of Links Abstract and 1 Introduction 1.1 Configuration Parameter Tuning Challenges and 1.2 Contributions 2 Tuning Objectives 3 Overview of Tuning Framework 4 Workload Characterization and 4.1 Query-level Characterization 4.2 Runtime-based Characterization 5 Feature Pruning and 5.1 Workload-level Pruning 5.2