Google Gemini 1.5
Last updated
Last updated
Copyright Continuum Labs - 2023
Performance:
Gemini 1.5 Flash demonstrates increasing efficiency in learning context for Kalamang ASR, with CER (Character Error Rate) improving with the addition of text and audio context.
Despite being lighter than 1.5 Pro, Gemini 1.5 Flash achieves significant accuracy in ASR tasks.
Analysis:
The model shows a significant improvement in ASR tasks with more context. It performs better in handling text and audio contexts, particularly in complex tasks like speech segmentation and word spelling.
Performance:
Gemini 1.5 Pro shows remarkable performance in translating low-resource languages.
The model's translation accuracy improves consistently with an increasing number of in-context examples, surpassing GPT-4 Turbo significantly.
Analysis:
The model's ability to leverage large context windows allows it to perform well in translating languages with limited pre-existing data. This demonstrates its capability to learn and adapt from in-context examples effectively.
Performance:
Gemini 1.5 Pro outperforms both Gemini 1.0 Pro and GPT-4 Turbo in answering questions from long documents like "Les Misérables."
The model maintains context over extensive texts and provides high-quality answers without the need for retrieval-augmented generation (RAG).
Analysis:
The ability to handle entire books as input showcases the model’s robustness in maintaining context over long passages and comprehensively understanding narratives and relationships within texts.
Performance:
Gemini 1.5 Pro achieves a WER (Word Error Rate) of 5.5% on 15-minute video transcriptions, outperforming models like USM and Whisper.
Gemini 1.5 Flash performs admirably with a WER of 8.8%, considering its smaller size.
Analysis:
The model’s long-context capabilities allow it to transcribe longer audio segments accurately without additional segmentation and pre-processing.
Performance:
Gemini 1.5 Pro achieves state-of-the-art accuracy in long-video QA tasks, significantly outperforming GPT-4V.
The model’s performance improves as more frames are provided, demonstrating its effectiveness in handling extended video contexts.
Analysis:
The model's ability to handle long video contexts and answer questions based on extensive video content makes it highly effective for applications in multimedia analysis and video understanding.
Performance:
Gemini 1.5 Pro outperforms other models in planning tasks expressed in PDDL (Planning Domain Definition Language) and natural language.
The model's performance improves with more examples, highlighting its effectiveness in in-context learning for planning tasks.
Analysis:
The model's capability to generate plans based on in-context examples showcases its potential in applications requiring strategic reasoning and decision-making.
Performance:
Gemini 1.5 Pro demonstrates superior performance in extracting structured information from unstructured data like images.
The model's accuracy improves with larger context windows, outperforming GPT-4 Turbo and Claude 3 Opus.
Analysis:
The model’s ability to process and analyze unstructured data efficiently makes it valuable for tasks involving large datasets of images, conversations, and other non-text data.
Cost:
Gemini 1.5 Flash is designed to be more cost-efficient compared to Gemini 1.5 Pro, making it suitable for high-volume, high-frequency tasks.
Usage:
The models can be accessed via API, with costs typically based on the number of tokens processed or the duration of usage. Specific pricing details should be obtained from the official pricing page or by contacting the sales team.
Prompt Techniques:
Clear and Specific Instructions:
Contextual Prompts:
Multimodal Inputs:
Interactive Prompts:
Temperature: Controls the randomness of the output.
Max Tokens: Sets the maximum number of tokens to generate.
Top_p: Enables nucleus sampling, controlling the diversity of the output.
Frequency_penalty: Discourages repetition of the same phrases.
Presence_penalty: Encourages introducing new topics.
Gemini 1.5 Flash and Pro models represent significant advancements in handling multimodal data, leveraging long-context windows, and performing a variety of high-frequency, high-volume tasks efficiently. Their ability to excel in complex tasks such as machine translation, long-document QA, and video understanding makes them invaluable tools for a wide range of applications in natural language processing and artificial intelligence.