Computer Science > Computation and Language
[Submitted on 8 Mar 2024 (v1), last revised 8 Aug 2024 (this version, v4)]
Title:Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
View PDFAbstract:In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.
Submission history
From: Alex Goldin [view email][v1] Fri, 8 Mar 2024 18:54:20 UTC (7,059 KB)
[v2] Thu, 25 Apr 2024 16:34:26 UTC (21,758 KB)
[v3] Fri, 14 Jun 2024 10:14:10 UTC (15,842 KB)
[v4] Thu, 8 Aug 2024 13:25:56 UTC (26,239 KB)
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