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A Survey of Multimodel Large Language Models
With the widespread application of the Transformer architecture in various modalities,
including vision, the technology of large language models is evolving from a single modality …
including vision, the technology of large language models is evolving from a single modality …
[HTML][HTML] A survey of GPT-3 family large language models including ChatGPT and GPT-4
Large language models (LLMs) are a special class of pretrained language models (PLMs)
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …
obtained by scaling model size, pretraining corpus and computation. LLMs, because of their …
Qwen technical report
Large language models (LLMs) have revolutionized the field of artificial intelligence,
enabling natural language processing tasks that were previously thought to be exclusive to …
enabling natural language processing tasks that were previously thought to be exclusive to …
[PDF][PDF] The dawn of lmms: Preliminary explorations with gpt-4v (ision)
Large multimodal models (LMMs) extend large language models (LLMs) with multi-sensory
skills, such as visual understanding, to achieve stronger generic intelligence. In this paper …
skills, such as visual understanding, to achieve stronger generic intelligence. In this paper …
Mm-vet: Evaluating large multimodal models for integrated capabilities
We propose MM-Vet, an evaluation benchmark that examines large multimodal models
(LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing …
(LMMs) on complicated multimodal tasks. Recent LMMs have shown various intriguing …
Vipergpt: Visual inference via python execution for reasoning
Answering visual queries is a complex task that requires both visual processing and
reasoning. End-to-end models, the dominant approach for this task, do not explicitly …
reasoning. End-to-end models, the dominant approach for this task, do not explicitly …
Multimodal foundation models: From specialists to general-purpose assistants
Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …
methods to data compression. Recent advances in statistical machine learning have opened …
Visual chatgpt: Talking, drawing and editing with visual foundation models
ChatGPT is attracting a cross-field interest as it provides a language interface with
remarkable conversational competency and reasoning capabilities across many domains …
remarkable conversational competency and reasoning capabilities across many domains …
Obelics: An open web-scale filtered dataset of interleaved image-text documents
Large multimodal models trained on natural documents, which interleave images and text,
outperform models trained on image-text pairs on various multimodal benchmarks …
outperform models trained on image-text pairs on various multimodal benchmarks …
Visual programming: Compositional visual reasoning without training
We present VISPROG, a neuro-symbolic approach to solving complex and compositional
visual tasks given natural language instructions. VISPROG avoids the need for any task …
visual tasks given natural language instructions. VISPROG avoids the need for any task …