arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2310.08166cs.CL
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Ziya-Visual:通过多任务指令微调实现的双语大型视觉-语言模型

Ziya-Visual: Bilingual Large Vision-Language Model via Multi-Task Instruction Tuning

International Digital Economy Academy(国际数字经济学院);University of Science and Technology of China(中国科学技术大学);South China University of Technology(华南理工大学)

Junyu Lu, Dixiang Zhang, Xiaojun Wu, Xinyu Gao, Ruyi Gan, Jiaxing Zhang, Yan Song, Pingjian Zhang

AI总结:

本文提出Ziya-Visual系列双语视觉-语言模型,通过多任务指令微调、多阶段训练和低秩适配实现视觉-语言对齐,并利用GPT-4生成中文多模态指令数据,在多项英文及中文多模态任务中取得有竞争力的性能。

摘要:

近期进展通过整合多模态输入,扩大了大语言模型(LLMs)在零样本图像到文本生成与理解方面的能力。然而,由于缺乏大规模高质量的非英语多模态资源,此类成功通常仅限于英语场景,这使得在其他语言中建立有竞争力的对应模型极为困难。本文介绍了Ziya-Visual系列,这是一组双语大规模视觉-语言模型(LVLMs),旨在将视觉语义融入LLM以进行多模态对话。我们的模型由Ziya-Visual-Base和Ziya-Visual-Chat组成,采用BLIP-2中的Querying Transformer,并进一步探索了指令微调、多阶段训练和低秩适配模块等优化方案对视觉-语言对齐的辅助作用。此外,我们激发了GPT-4在多模态场景中的理解能力,将收集的英文图像-文本数据集翻译为中文,并通过上下文学习方法生成指令-响应。实验结果表明,与现有LVLMs相比,Ziya-Visual在广泛的仅英语任务上取得了有竞争力的性能,包括零样本图像-文本检索、图像描述和视觉问答。由GPT-4访问的评估排行榜也表明,我们的模型在中文多模态场景对话中具备令人满意的图像-文本理解与生成能力。代码、演示和模型可在~\url{https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1}获取。

英文摘要:

Recent advancements enlarge the capabilities of large language models (LLMs) in zero-shot image-to-text generation and understanding by integrating multi-modal inputs. However, such success is typically limited to English scenarios due to the lack of large-scale and high-quality non-English multi-modal resources, making it extremely difficult to establish competitive counterparts in other languages. In this paper, we introduce the Ziya-Visual series, a set of bilingual large-scale vision-language models (LVLMs) designed to incorporate visual semantics into LLM for multi-modal dialogue. Composed of Ziya-Visual-Base and Ziya-Visual-Chat, our models adopt the Querying Transformer from BLIP-2, further exploring the assistance of optimization schemes such as instruction tuning, multi-stage training and low-rank adaptation module for visual-language alignment. In addition, we stimulate the understanding ability of GPT-4 in multi-modal scenarios, translating our gathered English image-text datasets into Chinese and generating instruction-response through the in-context learning method. The experiment results demonstrate that compared to the existing LVLMs, Ziya-Visual achieves competitive performance across a wide range of English-only tasks including zero-shot image-text retrieval, image captioning, and visual question answering. The evaluation leaderboard accessed by GPT-4 also indicates that our models possess satisfactory image-text understanding and generation capabilities in Chinese multi-modal scenario dialogues. Code, demo and models are available at ~\url{https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1}.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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