Connect with us

SEO

Google’s Chain of Thought Prompting Can Boost Today’s Best Algorithms

Published

on

Google announced a breakthrough research in Natural Language Processing called Chain of Thought Prompting that raises the state of the art of advanced technologies like PaLM and LaMDA to what the researchers call a remarkable level.

The fact that Chain of Thought Prompting can improve PaLM and LaMDA at these significant rates is a big deal.

LaMDA and PaLM

The research conducted experiments using two language models, Language Model for Dialogue Applications (LaMDA) and Pathways Language Model (PaLM).

LaMDA is a model focused on conversation, like a chatbot but also can be used for many other applications that require speaking, dialogue.

PaLM is a model that follows what Google calls the Pathways AI architecture where a language model is trained to learn how to solve problems.

Previously machine learning models were trained to solve one kind of problem and they’d be set loose essentially to do that one thing really well. But in order to do something else Google would have to train a new model.

Advertisement

The Pathways AI architecture is a way to create a model that can solve problems that it hasn’t necessarily seen before.

As quoted in the Google PaLM explainer:

“…we’d like to train one model that can not only handle many separate tasks, but also draw upon and combine its existing skills to learn new tasks faster and more effectively.”

What it Does

The research paper lists three important breakthroughs for Chain of Thought Reasoning:

  1. It allows language models to break down complex multi-step problems into a sequence of steps
  2. The chain of the thought process allows engineers to peek into the process and when things go wrong, this allows them to identify where it went wrong and fix it
  3. Can solve math word problems, can accomplish commonsense reasoning and according to the research paper can (in principle) solve any word-based problem that a human can.

Multi-step Reasoning Tasks

The research gives an example of a multi-step reasoning task that language models are tested on:

“Q: The cafeteria had 23 apples. If they used 20 to make lunch and bought 6 more, how many apples do they have?

A: The cafeteria had 23 apples originally. They used 20 to make lunch. So they had 23 – 20 = 3. They bought 6 more apples, so they have 3 + 6 = 9. The answer is 9.”

PaLM is a state of the art language model that is part of the Pathways AI architecture. It is so advanced it can explain why a joke is funny.

Yet, as advanced as PaLM is, the researchers claim that the Chain of Thought Prompting significantly improves these models, and that’s what makes this new research so worthy of taking note of.
Google explains it like this:

Advertisement

“Chain of thought reasoning allows models to decompose complex problems into intermediate steps that are solved individually.

Moreover, the language-based nature of chain of thought makes it applicable to any task that a person could solve via language.”

The research paper then goes on to note that standard prompting doesn’t really improve when the scale of the model is increased.

However with this new approach scale has a significant and notable positive impact on how well the model performs.

Results

Chain of Thought Prompting was tested on both LaMDA and PaLM, using two mathematical word problem datasets.

These datasets are used by researchers as a way to compare results on similar problems for different language models.

Below are images of graphs showing the results of using Chain of Thought Prompting on LaMDA.

Advertisement

Chain of Thought Prompting and LaMDA

The results of scaling LaMDA on the MultiArith dataset shows that it resulted modest improvement. But LaMDA scores significantly higher when scaled with Chain of Thought Prompting.

The results on the GSM8K dataset show a modest improvement.

It’s a different story with the PaLM language model.

Chain of Thought Prompting and PaLM

Chain of Thought Prompting and PaLM

As can be seen in the graph above the gains from scaling PaLM with Chain of Thought Prompting are huge, and they are huge for both datasets  (MultiArith and GSM8K).

The researchers call the results remarkable and a new state of the art:

“On the GSM8K dataset of math word problems, PaLM shows remarkable performance when scaled to 540B parameters.

…combining chain of thought prompting with the 540B parameter PaLM model leads to new state-of-the-art performance of 58%, surpassing the prior state of the art of 55% achieved by fine-tuning GPT-3 175B on a large training set and then ranking potential solutions via a specially trained verifier.

Advertisement

Moreover, follow-up work on self-consistency shows that the performance of chain of thought prompting can be improved further by taking the majority vote of a broad set of generated reasoning processes, which results in 74% accuracy on GSM8K.”

Conclusions

The conclusion of a research paper is one of the most important parts to check for understanding if the research advances the state of the art or is a dead-end or needs more research.

Google’s research paper conclusion section has a strongly positive note.

It notes:

“We have explored chain of thought prompting as a simple and broadly applicable method for enhancing reasoning in language models.

Through experiments on arithmetic, symbolic, and commonsense reasoning, we find that chain of thought processing is an emergent property of model scale that allows sufficiently large language models to perform reasoning tasks that otherwise have flat scaling curves.

Broadening the range of reasoning tasks that language models can perform will hopefully inspire further work on language-based approaches to reasoning.”

Advertisement

What that means is that Chain of Thought Prompting may have the potential to provide Google with the ability to significantly improve their various language models, which in turn can lead to significant improvements in the kinds of things Google can do.

Citations

Read the Google AI Article

Language Models Perform Reasoning via Chain of Thought

Download and Read the Research Paper

Chain of Thought Prompting Elicits Reasoning in Large Language Models (PDF)

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address

SEO

brightonSEO Live Blog

Published

on

brightonSEO Live Blog

Hello everyone. It’s April again, so I’m back in Brighton for another two days of sun, sea, and SEO!

Being the introvert I am, my idea of fun isn’t hanging around our booth all day explaining we’ve run out of t-shirts (seriously, you need to be fast if you want swag!). So I decided to do something useful and live-blog the event instead.

Follow below for talk takeaways and (very) mildly humorous commentary. 

Advertisement

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

SEO

Google Further Postpones Third-Party Cookie Deprecation In Chrome

Published

on

By

Close-up of a document with a grid and a red stamp that reads "delayed" over the word "status" due to Chrome's deprecation of third-party cookies.

Google has again delayed its plan to phase out third-party cookies in the Chrome web browser. The latest postponement comes after ongoing challenges in reconciling feedback from industry stakeholders and regulators.

The announcement was made in Google and the UK’s Competition and Markets Authority (CMA) joint quarterly report on the Privacy Sandbox initiative, scheduled for release on April 26.

Chrome’s Third-Party Cookie Phaseout Pushed To 2025

Google states it “will not complete third-party cookie deprecation during the second half of Q4” this year as planned.

Instead, the tech giant aims to begin deprecating third-party cookies in Chrome “starting early next year,” assuming an agreement can be reached with the CMA and the UK’s Information Commissioner’s Office (ICO).

The statement reads:

Advertisement

“We recognize that there are ongoing challenges related to reconciling divergent feedback from the industry, regulators and developers, and will continue to engage closely with the entire ecosystem. It’s also critical that the CMA has sufficient time to review all evidence, including results from industry tests, which the CMA has asked market participants to provide by the end of June.”

Continued Engagement With Regulators

Google reiterated its commitment to “engaging closely with the CMA and ICO” throughout the process and hopes to conclude discussions this year.

This marks the third delay to Google’s plan to deprecate third-party cookies, initially aiming for a Q3 2023 phaseout before pushing it back to late 2024.

The postponements reflect the challenges in transitioning away from cross-site user tracking while balancing privacy and advertiser interests.

Transition Period & Impact

In January, Chrome began restricting third-party cookie access for 1% of users globally. This percentage was expected to gradually increase until 100% of users were covered by Q3 2024.

However, the latest delay gives websites and services more time to migrate away from third-party cookie dependencies through Google’s limited “deprecation trials” program.

The trials offer temporary cookie access extensions until December 27, 2024, for non-advertising use cases that can demonstrate direct user impact and functional breakage.

Advertisement

While easing the transition, the trials have strict eligibility rules. Advertising-related services are ineligible, and origins matching known ad-related domains are rejected.

Google states the program aims to address functional issues rather than relieve general data collection inconveniences.

Publisher & Advertiser Implications

The repeated delays highlight the potential disruption for digital publishers and advertisers relying on third-party cookie tracking.

Industry groups have raised concerns that restricting cross-site tracking could push websites toward more opaque privacy-invasive practices.

However, privacy advocates view the phaseout as crucial in preventing covert user profiling across the web.

With the latest postponement, all parties have more time to prepare for the eventual loss of third-party cookies and adopt Google’s proposed Privacy Sandbox APIs as replacements.

Advertisement

Featured Image: Novikov Aleksey/Shutterstock

Source link

Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

SEO

How To Write ChatGPT Prompts To Get The Best Results

Published

on

By

How To Write ChatGPT Prompts To Get The Best Results

ChatGPT is a game changer in the field of SEO. This powerful language model can generate human-like content, making it an invaluable tool for SEO professionals.

However, the prompts you provide largely determine the quality of the output.

To unlock the full potential of ChatGPT and create content that resonates with your audience and search engines, writing effective prompts is crucial.

In this comprehensive guide, we’ll explore the art of writing prompts for ChatGPT, covering everything from basic techniques to advanced strategies for layering prompts and generating high-quality, SEO-friendly content.

Writing Prompts For ChatGPT

What Is A ChatGPT Prompt?

A ChatGPT prompt is an instruction or discussion topic a user provides for the ChatGPT AI model to respond to.

Advertisement

The prompt can be a question, statement, or any other stimulus to spark creativity, reflection, or engagement.

Users can use the prompt to generate ideas, share their thoughts, or start a conversation.

ChatGPT prompts are designed to be open-ended and can be customized based on the user’s preferences and interests.

How To Write Prompts For ChatGPT

Start by giving ChatGPT a writing prompt, such as, “Write a short story about a person who discovers they have a superpower.”

ChatGPT will then generate a response based on your prompt. Depending on the prompt’s complexity and the level of detail you requested, the answer may be a few sentences or several paragraphs long.

Use the ChatGPT-generated response as a starting point for your writing. You can take the ideas and concepts presented in the answer and expand upon them, adding your own unique spin to the story.

Advertisement

If you want to generate additional ideas, try asking ChatGPT follow-up questions related to your original prompt.

For example, you could ask, “What challenges might the person face in exploring their newfound superpower?” Or, “How might the person’s relationships with others be affected by their superpower?”

Remember that ChatGPT’s answers are generated by artificial intelligence and may not always be perfect or exactly what you want.

However, they can still be a great source of inspiration and help you start writing.

Must-Have GPTs Assistant

I recommend installing the WebBrowser Assistant created by the OpenAI Team. This tool allows you to add relevant Bing results to your ChatGPT prompts.

This assistant adds the first web results to your ChatGPT prompts for more accurate and up-to-date conversations.

Advertisement

It is very easy to install in only two clicks. (Click on Start Chat.)

Screenshot from ChatGPT, April 2024

For example, if I ask, “Who is Vincent Terrasi?,” ChatGPT has no answer.

With WebBrower Assistant, the assistant creates a new prompt with the first Bing results, and now ChatGPT knows who Vincent Terrasi is.

Enabling reverse prompt engineeringScreenshot from ChatGPT, March 2023

You can test other GPT assistants available in the GPTs search engine if you want to use Google results.

Master Reverse Prompt Engineering

ChatGPT can be an excellent tool for reverse engineering prompts because it generates natural and engaging responses to any given input.

By analyzing the prompts generated by ChatGPT, it is possible to gain insight into the model’s underlying thought processes and decision-making strategies.

One key benefit of using ChatGPT to reverse engineer prompts is that the model is highly transparent in its decision-making.

Advertisement

This means that the reasoning and logic behind each response can be traced, making it easier to understand how the model arrives at its conclusions.

Once you’ve done this a few times for different types of content, you’ll gain insight into crafting more effective prompts.

Prepare Your ChatGPT For Generating Prompts

First, activate the reverse prompt engineering.

  • Type the following prompt: “Enable Reverse Prompt Engineering? By Reverse Prompt Engineering I mean creating a prompt from a given text.”
Enabling reverse prompt engineeringScreenshot from ChatGPT, March 2023

ChatGPT is now ready to generate your prompt. You can test the product description in a new chatbot session and evaluate the generated prompt.

  • Type: “Create a very technical reverse prompt engineering template for a product description about iPhone 11.”
Reverse Prompt engineering via WebChatGPTScreenshot from ChatGPT, March 2023

The result is amazing. You can test with a full text that you want to reproduce. Here is an example of a prompt for selling a Kindle on Amazon.

  • Type: “Reverse Prompt engineer the following {product), capture the writing style and the length of the text :
    product =”
Reverse prompt engineering: Amazon productScreenshot from ChatGPT, March 2023

I tested it on an SEJ blog post. Enjoy the analysis – it is excellent.

  • Type: “Reverse Prompt engineer the following {text}, capture the tone and writing style of the {text} to include in the prompt :
    text = all text coming from https://www.searchenginejournal.com/google-bard-training-data/478941/”
Reverse prompt engineering an SEJ blog postScreenshot from ChatGPT, March 2023

But be careful not to use ChatGPT to generate your texts. It is just a personal assistant.

Go Deeper

Prompts and examples for SEO:

  • Keyword research and content ideas prompt: “Provide a list of 20 long-tail keyword ideas related to ‘local SEO strategies’ along with brief content topic descriptions for each keyword.”
  • Optimizing content for featured snippets prompt: “Write a 40-50 word paragraph optimized for the query ‘what is the featured snippet in Google search’ that could potentially earn the featured snippet.”
  • Creating meta descriptions prompt: “Draft a compelling meta description for the following blog post title: ’10 Technical SEO Factors You Can’t Ignore in 2024′.”

Important Considerations:

  • Always Fact-Check: While ChatGPT can be a helpful tool, it’s crucial to remember that it may generate inaccurate or fabricated information. Always verify any facts, statistics, or quotes generated by ChatGPT before incorporating them into your content.
  • Maintain Control and Creativity: Use ChatGPT as a tool to assist your writing, not replace it. Don’t rely on it to do your thinking or create content from scratch. Your unique perspective and creativity are essential for producing high-quality, engaging content.
  • Iteration is Key: Refine and revise the outputs generated by ChatGPT to ensure they align with your voice, style, and intended message.

Additional Prompts for Rewording and SEO:
– Rewrite this sentence to be more concise and impactful.
– Suggest alternative phrasing for this section to improve clarity.
– Identify opportunities to incorporate relevant internal and external links.
– Analyze the keyword density and suggest improvements for better SEO.

Remember, while ChatGPT can be a valuable tool, it’s essential to use it responsibly and maintain control over your content creation process.

Experiment And Refine Your Prompting Techniques

Writing effective prompts for ChatGPT is an essential skill for any SEO professional who wants to harness the power of AI-generated content.

Advertisement

Hopefully, the insights and examples shared in this article can inspire you and help guide you to crafting stronger prompts that yield high-quality content.

Remember to experiment with layering prompts, iterating on the output, and continually refining your prompting techniques.

This will help you stay ahead of the curve in the ever-changing world of SEO.

More resources: 


Featured Image: Tapati Rinchumrus/Shutterstock

Source link

Advertisement
Keep an eye on what we are doing
Be the first to get latest updates and exclusive content straight to your email inbox.
We promise not to spam you. You can unsubscribe at any time.
Invalid email address
Continue Reading

Trending

Follow by Email
RSS