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Google:Gemini 1.5技术报告(英文版)(154页).pdf

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1、Gemini 1.5:Unlocking multimodalunderstanding across millions of tokens ofcontextGemini Team,Google1In this report,we introduce the Gemini 1.5 family of models,representing the next generation of highlycompute-efficient multimodal models capable of recalling and reasoning over fine-grained informatio

2、nfrom millions of tokens of context,including multiple long documents and hours of video and audio.Thefamily includes two new models:(1)an updated Gemini 1.5 Pro,which exceeds the February version onthe great majority of capabilities and benchmarks;(2)Gemini 1.5 Flash,a more lightweight variantdesig

3、ned for efficiency with minimal regression in quality.Gemini 1.5 models achieve near-perfectrecall on long-context retrieval tasks across modalities,improve the state-of-the-art in long-documentQA,long-video QA and long-context ASR,and match or surpass Gemini 1.0 Ultras state-of-the-artperformance a

4、cross a broad set of benchmarks.Studying the limits of Gemini 1.5s long-context ability,we find continued improvement in next-token prediction and near-perfect retrieval(99%)up to atleast 10M tokens,a generational leap over existing models such as Claude 3.0(200k)and GPT-4 Turbo(128k).Finally,we hig

5、hlight real-world use cases,such as Gemini 1.5 collaborating with professionalson completing their tasks achieving 26 to 75%time savings across 10 different job categories,as well assurprising new capabilities of large language models at the frontier;when given a grammar manual forKalamang,a languag

6、e with fewer than 200 speakers worldwide,the model learns to translate English toKalamang at a similar level to a person who learned from the same content.1.IntroductionWe present our latest multimodal models from the Gemini line:Gemini 1.5 Pro and Gemini 1.5Flash.They are members of Gemini 1.5,a ne

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本文介绍了Gemini 1.5系列模型,包括Gemini 1.5 Pro和Gemini 1.5 Flash。这些模型代表了下一代高效的多模态模型,能够回忆和推理来自数百万个上下文标记(包括多个长文档和数小时的视频和音频)的细粒度信息。Gemini 1.5 Pro在大多数能力和基准测试中超过了之前的版本,而Gemini 1.5 Flash则是一个更轻量级的版本,设计用于提高效率,同时对质量的影响最小。Gemini 1.5模型在长上下文检索任务中实现了近乎完美的召回率,在长文档QA、长视频QA和长上下文ASR方面改进了最先进的技术,并在一系列基准测试中与Gemini 1.0 Ultra的先进性能相匹配或超越。 Gemini 1.5 Pro和Gemini 1.5 Flash在长上下文能力方面取得了显著的进步,例如,在100万标记的上下文中实现近100%的召回率,并在1000万标记的上下文中保持99.2%的召回率。这些模型还展示了在长文档、长视频和长音频中的新能力,例如,仅通过一本参考语法书和双语词汇表学习将英语翻译成卡拉芒语,以及从单个视频帧中提取信息。 总的来说,Gemini 1.5系列模型在多模态理解和长上下文处理方面取得了重大突破,为处理更复杂和更长的多模态输入提供了新的可能性。
谷歌Gemini 1.5模型如何实现多模态理解? Gemini 1.5模型在长文本处理方面有何优势? 谷歌Gemini 1.5模型如何助力新语言学习?
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