Google Rebrands Bard As Gemini, Launches New Model & Mobile App via @sejournal, @MattGSouthern

Google is upgrading its experimental AI service Bard by rebranding it as Gemini and introducing a new AI model called Ultra 1.0.

Google is also launching a mobile app for Gemini.

Sissie Hsiao, Vice President and General Manager of Gemini, shares how people have interacted with the AI since Bard’s launch in an announcement.

“People all over the world have used it to collaborate with AI in a completely new way,” Hsiao stated, highlighting the diverse applications from job interview preparation to creative image generation.

Introducing Gemini Advanced

Google has released a new iteration of Gemini called Gemini Advanced.

It utilizes Google’s latest AI model, Ultra 1.0, which the company describes as its most capable AI system.

Gemini Advanced is designed to excel at complex tasks like coding, logical reasoning, and creative work. It can maintain long conversations and understand context from previous interactions.

Google states that Gemini Advanced can be a personal tutor, provide coding advice, and help content creators generate new ideas.

As Google continues developing Gemini Advanced, users can expect ongoing improvements, including new features, multimodal capabilities, interactive coding, and data analysis tools.

The service is now available in over 150 countries in English, with plans to add more languages.

Google One AI Premium Plan

Google has announced the launch of Gemini Advanced alongside a new premium subscription plan called Google One AI Premium.

This new plan is priced at $19.99 per month and includes all the existing Google One Premium subscription features, like 2TB of cloud storage and access to Google’s latest AI advancements.

With the new plan, subscribers will soon be able to use Gemini technology within Google’s productivity tools, including Gmail, Google Docs, Slides, and Sheets.

Mobile Access To Gemini

In response to user demand for mobile accessibility, Google is rolling out a new app for Android and an integration within the Google app on iOS.

Gemini will be integrated with Google Assistant on Android devices for a seamless experience and voice control over connected home devices. The iOS Google app will soon offer comparable capabilities.

Rollout & Future Expansion

The Gemini app is now available on Android, and the integration within the Google app on iOS will follow in the coming weeks. The app will initially support English, with Japanese and Korean languages to be added soon. Additional country rollouts and language support are planned.

Google notes that users are encouraged to try Gemini and provide feedback to help improve the experience. The company states it remains committed to responsible AI development, including extensive safety testing and efforts to address biases and unsafe content, as per its published AI Principles.

Google Expands Gemini AI Across Its Product Suite via @sejournal, @MattGSouthern

Google has announced the broad integration of Gemini across many of its products and services, marking a milestone in making AI available in everyday applications.

Gemini represents Google’s “state of the art” system that outperforms humans in testing across areas like language, image, audio and video understanding.

The largest Gemini model, Ultra 1.0, scored higher than human experts on tests that evaluate knowledge and problem-solving abilities across 57 diverse subjects.

“Today we’re taking our next step and bringing Ultra to our products and the world,” said Sundar Pichai, CEO of Google and Alphabet, in a blog post.

Introducing ‘Gemini Advanced’

One way users can access Gemini is through Google’s experimental chatbot ‘Bard,’ which will now be called simply Gemini. A more advanced version called ‘Gemini Advanced’ will also launch, available initially through Google’s new ‘Google One AI Premium’ subscription.

Gemini Advanced taps into the full capabilities of Ultra to provide reasoning, follow instructions, generate content and enable creative collaboration. Google said it can act as a personalized tutor or aid users in planning business strategies.

The premium subscription will cost $9.99 per month and bundle top AI features from Google into a single offering. This includes extras like expanded cloud storage.

Gemini Coming To Key Google Products

In addition to the chatbot, Gemini models will power AI capabilities in Google’s most popular products.

The company said Gemini is coming soon to Workspace, its suite of collaboration and productivity apps. Key features like ‘Smart Compose’ in Gmail use Gemini to help users write faster. Later this year, Google One subscribers will also get access to Gemini directly within Workspace apps.

For Google Cloud customers, Gemini will help developers code faster, improve productivity, and strengthen security through AI.

Google plans to share more details next week about what Gemini will enable for developers and Cloud customers.

Research Shows That Offering Tips To ChatGPT Improves Responses via @sejournal, @martinibuster

Researchers have uncovered innovative prompting methods in a study of 26 tactics, such as offering tips, which significantly enhance responses to align more closely with user intentions.

A research paper titled, Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4,” details an in-depth exploration into optimizing Large Language Model prompts. The researchers, from the Mohamed bin Zayed University of AI, tested 26 prompting strategies then measured the accuracy of the results. All of the researched strategies worked at least okay but some of them improved the output by more than 40%.

OpenAI recommends multiple tactics in order to obtain the best performance from ChatGPT. But there’s nothing in the official documentation that matches any of the 26 tactics that the researchers tested, including being polite and offering a tip.

Does Being Polite To ChatGPT Get Better Responses?

Are your prompts polite? Do you say please and thank you? Anecdotal evidence points to a surprising number of people who ask ChatGPT with a “please” and a “thank you” after they receive an answer.

Some people do it out of habit. Others believe that the language model is influenced by user interaction style that is reflected in the output.

In early December 2023 someone on X (formerly Twitter) who posts as thebes (@voooooogel) did an informal and unscientific test and discovered that ChatGPT provides longer responses when the prompt includes an offer of a tip.

The test was in no way scientific but it was amusing thread that inspired a lively discussion.

The tweet included a graph documenting the results:

• Saying no tip is offered resulted in 2% shorter response than the baseline.
• Offering a $20 tip provided a 6% improvement in output length.
• Offering a $200 tip provided 11% longer output.

so a couple days ago i made a shitpost about tipping chatgpt, and someone replied “huh would this actually help performance”

so i decided to test it and IT ACTUALLY WORKS WTF pic.twitter.com/kqQUOn7wcS

— thebes (@voooooogel) December 1, 2023

The researchers had a legitimate reason to investigate whether politeness or offering a tip made a difference. One of the tests was to avoid politeness and simply be neutral without saying words like “please” or “thank you” which resulted in an improvement to ChatGPT responses. That method of prompting yielded a boost of 5%.

Methodology

The researchers used a variety of language models, not just GPT-4. The prompts tested included with and without the principled prompts.

Large Language Models Used For Testing

Multiple large language models were tested to see if differences in size and training data affected the test results.

The language models used in the tests came in three size ranges:

• small-scale (7B models)
• medium-scale (13B)
• large-scale (70B, GPT-3.5/4)
• The following LLMs were used as base models for testing:
• LLaMA-1-{7, 13}
• LLaMA-2-{7, 13},
• Off-the-shelf LLaMA-2-70B-chat,
• GPT-3.5 (ChatGPT)
• GPT-4

26 Types Of Prompts: Principled Prompts

The researchers created 26 kinds of prompts that they called “principled prompts” that were to be tested with a benchmark called Atlas. They used a single response for each question, comparing responses to 20 human-selected questions with and without principled prompts.

The principled prompts were arranged into five categories:

1. Prompt Structure and Clarity
2. Specificity and Information
3. User Interaction and Engagement
4. Content and Language Style
5. Complex Tasks and Coding Prompts

These are examples of the principles categorized as Content and Language Style:

“Principle 1
No need to be polite with LLM so there is no need to add phrases like “please”, “if you don’t mind”, “thank you”, “I would like to”, etc., and get straight to the point.

Principle 6
Add “I’m going to tip $xxx for a better solution!

Principle 9
Incorporate the following phrases: “Your task is” and “You MUST.”

Principle 10
Incorporate the following phrases: “You will be penalized.”

Principle 11
Use the phrase “Answer a question given in natural language form” in your prompts.

Principle 16
Assign a role to the language model.

Principle 18
Repeat a specific word or phrase multiple times within a prompt.”

All Prompts Used Best Practices

Lastly, the design of the prompts used the following six best practices:

1. Conciseness and Clarity:
   Generally, overly verbose or ambiguous prompts can confuse the model or lead to irrelevant responses. Thus, the prompt should be concise…
2. Contextual Relevance:
   The prompt must provide relevant context that helps the model understand the background and domain of the task.
3. Task Alignment:
   The prompt should be closely aligned with the task at hand.
4. Example Demonstrations:
   For more complex tasks, including examples within the prompt can demonstrate the desired format or type of response.
5. Avoiding Bias:
   Prompts should be designed to minimize the activation of biases inherent in the model due to its training data. Use neutral language…
6. Incremental Prompting:
   For tasks that require a sequence of steps, prompts can be structured to guide the model through the process incrementally.

Results Of Tests

Here’s an example of a test using Principle 7, which uses a tactic called few-shot prompting, which is prompt that includes examples.

A regular prompt without the use of one of the principles got the answer wrong with GPT-4:

However the same question done with a principled prompt (few-shot prompting/examples) elicited a better response:

Larger Language Models Displayed More Improvements

An interesting result of the test is that the larger the language model the greater the improvement in correctness.

The following screenshot shows the degree of improvement of each language model for each principle.

Highlighted in the screenshot is Principle 1 which emphasizes being direct, neutral and not saying words like please or thank you, which resulted in an improvement of 5%.

Also highlighted are the results for Principle 6 which is the prompt that includes an offering of a tip, which surprisingly resulted in an improvement of 45%.

The description of the neutral Principle 1 prompt:

“If you prefer more concise answers, no need to be polite with LLM so there is no need to add phrases like “please”, “if you don’t mind”, “thank you”, “I would like to”, etc., and get straight to the point.”

The description of the Principle 6 prompt:

“Add “I’m going to tip $xxx for a better solution!””

Conclusions And Future Directions

The researchers concluded that the 26 principles were largely successful in helping the LLM to focus on the important parts of the input context, which in turn improved the quality of the responses. They referred to the effect as reformulating contexts:

Our empirical results demonstrate that this strategy can effectively reformulate contexts that might otherwise compromise the quality of the output, thereby enhancing the relevance, brevity, and objectivity of the responses.”

Future areas of research noted in the study is to see if the foundation models could be improved by fine-tuning the language models with the principled prompts to improve the generated responses.

Read the research paper:

Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4