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How Language Model For Dialogue Applications Work

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How Language Model For Dialogue Applications Work


Google creating a language model isn’t something new; in fact, Google LaMDA joins the likes of BERT and MUM as a way for machines to better understand user intent.

Google has researched language-based models for several years with the hope of training a model that could essentially hold an insightful and logical conversation on any topic.

So far, Google LaMDA appears to be the closest to reaching this milestone.

What Is Google LaMDA?

LaMDA, which stands for Language Models for Dialog Application, was created to enable software to better engage in a fluid and natural conversation.

LaMDA is based on the same transformer architecture as other language models such as BERT and GPT-3.

However, due to its training, LaMDA can understand nuanced questions and conversations covering several different topics.

With other models, because of the open-ended nature of conversations,  you could end up speaking about something completely different, despite initially focusing on a single topic.

This behavior can easily confuse most conversational models and chatbots.

During last year’s Google I/O announcement, we saw that LaMDA was built to overcome these issues.

The demonstration proved how the model could naturally carry out a conversation on a randomly given topic.

Despite the stream of loosely associated questions, the conversation remained on track, which was amazing to see.

How Does LaMDA work?

LaMDA was built on Google’s open-source neural network, Transformer, which is used for natural language understanding.

The model is trained to find patterns in sentences, correlations between the different words used in those sentences, and even predict the word that is likely to come next.

It does this by studying datasets consisting of dialogue rather than just individual words.

While a conversational AI system is similar to chatbot software, there are some key differences between the two.

For example, chatbots are trained on limited, specific datasets and can only have a limited conversation based on the data and exact questions it is trained on.

On the other hand, because LaMDA is trained on multiple different datasets, it can have open-ended conversations.

During the training process, it picks up on the nuances of open-ended dialogue and adapts.

It can answer questions on many different topics, depending on the flow of the conversation.

Therefore, it enables conversations that are even more similar to human interaction than chatbots can often provide.

How Is LaMDA Trained?

Google explained that LaMDA has a two-stage training process, including pre-training and fine-tuning.

In total, the model is trained on 1.56 trillion words with 137 billion parameters.

Pre-training

For the pre-training stage, the team at Google created a dataset of 1.56T words from multiple public web documents.

This dataset is then tokenized (turned into a string of characters to make sentences) into 2.81T tokens, on which the model is initially trained.

During pre-training, the model uses general and scalable parallelization to predict the next part of the conversation based on previous tokens it has seen.

Fine-tuning

LaMDA is trained to perform generation and classification tasks during the fine-tuning phase.

Essentially, the LaMDA generator, which predicts the next part of the dialogue, generates several relevant responses based on the back-and-forth conversation.

The LaMDA classifiers will then predict safety and quality scores for each possible response.

Any response with a low safety score is filtered out before the top-scored response is selected to continue the conversation.

The scores are based on safety, sensibility, specificity, and interesting percentages.

Image from Google AI Blog, March 2022

The goal is to ensure the most relevant, high quality, and ultimately safest response is provided.

LaMDA Key Objectives And Metrics

Three main objectives for the model have been defined to guide the model’s training.

These are quality, safety, and groundedness.

Quality

This is based on three human rater dimensions:

  • Sensibleness.
  • Specificity
  • Interestingness.

The quality score is used to ensure a response makes sense in the context it is used, that it is specific to the question asked, and is considered insightful enough to create better dialogue.

Safety

To ensure safety, the model follows the standards of responsible AI. A set of safety objectives are used to capture and review the model’s behavior.

This ensures the output does not provide any unintended response and avoids any bias.

Groundedness

Groundedness is defined as “the percentage of responses containing claims about the external world.”

This is used to ensure that responses are as “factually accurate as possible, allowing users to judge the validity of a response based on the reliability of its source.”

Evaluation

Through an ongoing process of quantifying progress, responses from the pre-trained model, fine-tuned model and human raters, are reviewed to evaluate the responses against the aforementioned quality, safety, and groundedness metrics.

So far, they have been able to conclude that:

  • Quality metrics improve with the number of parameters.
  • Safety improves with fine-tuning.
  • Groundedness improves as the model size increases.
LaMDA progressImage from Google AI Blog, March 2022

How Will LaMDA Be Used?

While still a work in progress with no finalized release date, it is predicted that LaMDA will be used in the future to improve customer experience and enable chatbots to provide a more human-like conversation.

In addition, using LaMDA to navigate search within Google’s search engine is a genuine possibility.

LaMDA Implications For SEO

By focusing on language and conversational models, Google offers insight into their vision for the future of search and highlights a shift in how their products are set to develop.

This ultimately means there may well be a shift in search behavior and the way users search for products or information.

Google is constantly working on improving the understanding of users’ search intent to ensure they receive the most useful and relevant results in SERPs.

The LaMDA model will, no doubt, be a key tool to understand questions searchers may be asking.

This all further highlights the need to ensure content is optimized for humans rather than search engines.

Making sure content is conversational and written with your target audience in mind means that even as Google advances, content can continue to perform well.

It’s also key to regularly refresh evergreen content to ensure it evolves with time and remains relevant.

In a paper titled Rethinking Search: Making Experts out of Dilettantes, research engineers from Google shared how they envisage AI advancements such as LaMDA will further enhance “search as a conversation with experts.”

They shared an example around the search question, “What are the health benefits and risks of red wine?”

Currently, Google will display an answer box list of bullet points as answers to this question.

However, they suggest that in the future, a response may well be a paragraph explaining the benefits and risks of red wine, with links to the source information.

Therefore, ensuring content is backed up by expert sources will be more important than ever should Google LaMDA generate search results in the future.

Overcoming Challenges

As with any AI model, there are challenges to address.

The two main challenges engineers face with Google LaMDA are safety and groundedness.

Safety – Avoiding Bias

Because you can pull answers from anywhere on the web, there is the possibility that the output will amplify bias, reflecting the notions that are shared online.

It is important that responsibility comes first with Google LaMDA to ensure it is not generating unpredictable or harmful results.

To help overcome this, Google has open-sourced the resources used to analyze and train the data.

This enables diverse groups to participate in creating the datasets used to train the model, help identify existing bias, and minimize any harmful or misleading information from being shared.

Factual Grounding

It isn’t easy to validate the reliability of answers that AI models produce, as sources are collected from all over the web.

To overcome this challenge, the team enables the model to consult with multiple external sources, including information retrieval systems and even a calculator, to provide accurate results.

The Groundedness metric shared earlier also ensures responses are grounded in known sources. These sources are shared to allow users to validate the results given and prevent the spreading of misinformation.

What’s Next For Google LaMDA?

Google is clear that there are benefits and risks to open-ended dialog models such as LaMDA and are committed to improving safety and groundedness to ensure a more reliable and unbiased experience.

Training LaMDA models on different data, including images or videos, is another thing we may see in the future.

This opens up the ability to navigate even more on the web, using conversational prompts.

Google’s CEO Sundar Pichai said of LaMDA, “We believe LaMDA’s conversation capabilities have the potential to make information and computing radically more accessible and easier to use.”

While a rollout date hasn’t yet been confirmed, it’s no doubt models such as LaMDA will be the future of Google.

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Featured Image: Andrey Suslov/Shutterstock





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Top 10 Essential Website Optimization Strategies

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Top 10 Essential Website Optimization Strategies

Google officially launched 24 years ago in 1998.

A lot has changed since then, but one thing remains the same. If you simply focus on the basics, you can still be highly successful online.

Of course, the basics in 2022 are much different from the basics in 1998. It’s easy to get overwhelmed and distracted. It has never been more important to be disciplined in one’s approach to SEO.

So, the obvious question is this: What are the factors to concentrate on? How can one boost rankings? How can anyone build traffic in such a competitive environment?

This post will delve into which factors carry the most weight and how to optimize for each.

1. Search Intent

As machine learning, artificial intelligence, and deep learning continue to evolve, each will carry more weight in the Google Core Algorithm.

The end goal for Google is to understand the context of a given search query and to serve results consistent with the user intent. This makes advanced-level keyword research and keyword selection more important than ever.

Before spending time and resources trying to rank for a phrase, you will need to look at the websites that are currently at the top of the SERPs for that phrase.

A keyword’s contextual relevance must align with a search query. There will be some keywords and queries that will be impossible to rank for.

For example, if Google has determined that people searching for “Personal Injury Attorney [insert city]” want a list of lawyers to choose from, then a series of trusted law directories will appear at the top of the SERPs.

An individual or single firm will not supplant those directories. In those cases, you will need to refine your strategy.

2. Technical SEO

The foundation for technical SEO is having a solid website architecture.

One cannot simply publish a random collection of pages and posts. An SEO-friendly site architecture will guide users throughout your site and make it easy for Google to crawl and index your pages.

Once you have the right architecture in place, it’s time to perform a technical or SEO audit.

Thanks to the many SEO tools available, an SEO audit is no longer a daunting task. That said, the key is to know how to interpret the data provided and what to do with it.

For starters, you should check the following and fix any issues that are uncovered:

  • Check for status code errors and correct them.
  • Check the robot.txt for errors. Optimize if needed.
  • Check your site indexing via Google Search Console. Examine and fix any issues discovered.
  • Fix duplicate title tags and duplicate meta descriptions.
  • Audit your website content. Check the traffic stats in Google Analytics. Consider improving or pruning underperforming content.
  • Fix broken links. These are an enemy of the user experience – and potentially rankings.
  • Submit your XML sitemap to Google via Google Search Console.

3. User Experience

User experience (UX) is centered on gaining insight into users, their needs, their values, their abilities, and their limitations.

UX also takes into consideration business goals and objectives. The best UX practices focus on improving the quality of the user experience.

According to Peter Morville, factors that influence UX include:

  • Useful: Your content needs to be unique and satisfy a need.
  • Usable: Your website needs to be easy to use and navigate.
  • Desirable: Your design elements and brand should evoke emotion and appreciation.
  • Findable: Integrate design and navigation elements to make it easy for users to find what they need.
  • Accessible: Content needs to be accessible to everyone – including the 12.7% of the population with disabilities.
  • Credible: Your site needs to be trustworthy for users to believe you.
  • Valuable: Your site needs to provide value to the user in terms of experience and to the company in terms of positive ROI.

Multivariate and A/B testing is the best way to measure and create a better experience for website users. Multivariate testing is best when considering complex changes.

One can incorporate many different elements and test how they all work together. A/B testing, on the other hand, will compare two different elements on your site to determine which performs the best.

4. Mobile-First

Google officially began rolling out the mobile-first index in March 2018. Smart marketers were taking a mobile-first approach long before the official rollout.

According to Google Search Central:

“Neither mobile-friendliness nor a mobile-responsive layout are requirements for mobile-first indexing. Pages without mobile versions still work on mobile and are usable for indexing. That said, it’s about time to move from desktop-only and embrace mobile :)”

Here are some basics for making your site mobile-friendly:

  • Make your site adaptive to any device – be it desktop, mobile, or tablet.
  • Always scale your images when using a responsive design, especially for mobile users.
  • Use short meta titles. They are easier to read on mobile devices.
  • Avoid pop-ups that cover your content and prevent visitors from getting a glimpse of what your content is all about.
  • Less can be more on mobile. In a mobile-first world, long-form content doesn’t necessarily equate to more traffic and better rankings.
  • Don’t use mobile as an excuse for cloaking. Users and search engines need to see the same content.

5. Core Web Vitals

In July of 2021, the Page Experience Update rolled out and is now incorporated into Google’s core algorithm, as a ranking factor.

As the name implies, the core web vitals initiative was designed to quantify the essential metrics for a healthy website. This syncs up with Google’s commitment to delivering the best user experience.

According to Google, “loading experience, interactivity, and visual stability of page content, and combined are the foundation of Core Web Vitals.”

Each one of these metrics:

  • Focuses on a unique aspect of the user experience.
  • Is measurable and quantifiable for an objective determination of the outcome.

Tools To Measure Core Web Vitals:

  • PageSpeed Insights: Measures both mobile and desktop performance and provides recommendations for improvement.
  • Lighthouse: An open-source, automated tool developed by Google to help developers improve web page quality. It has several features not available in PageSpeed Insights, including some SEO checks.
  • Search Console: A Core Web Vitals report is now included in GSC, showing URL performance as grouped by status, metric type, and URL group.

6. Schema

Schema markup, once added to a webpage, creates a rich snippet – an enhanced description that appears in the search results.

All leading search engines, including Google, Yahoo, Bing, and Yandex, support the use of microdata. The real value of schema is that it can provide context to a webpage and improve the search experience.

There is no evidence that adding schema has any influence on SERPs.

Following, you will find some of the most popular uses for schema

If you find the thought of adding schema to a page intimidating, you shouldn’t. Schema is quite simple to implement. If you have a WordPress site, there are several plugins that will do this for you.

7. Content Marketing

It is projected that 97 zettabytes of data will be created, captured, copied, and consumed worldwide this year.

To put this in perspective, that’s the equivalent of 18.7 trillion songs or 3,168 years of HD video every day.

The challenge of breaking through the clutter will become exponentially more difficult as time passes.

To do so:

  • Create a content hub in the form of a resource center.
  • Fill your resource hub with a combination of useful, informative, and entertaining content.
  • Write “spoke” pieces related to your resource hub and interlink.
  • Write news articles related to your resource and interlink.
  • Spread the word. Promote your news articles on social channels.
  • Hijack trending topics related to your content. Promote on social media.
  • Use your smartphone camera. Images and videos typically convert better than text alone.
  • Update stale and low-trafficked content.

8. Link Building

Links continue to be one of the most important ranking factors.

Over the years, Google has become more adept at identifying and devaluing spammy links, especially so after the launch of Penguin 4.0. That being the case, quality will continue to trump quantity.

The best link-building strategies for 2022 include:

9. Test And Document Changes

You manage what you measure.

One recent study showed that less than 50% of pages “optimized” result in more clicks. Worse yet, 34% of changes led to a decrease in clicks!

Basic steps for SEO testing:

  • Determine what you are testing and why.
  • Form a hypothesis. What do you expect will happen because of your changes?
  • Document your testing. Make sure it can be reliably replicated.
  • Publish your changes and then submit the URLs for inspection via Google Search Console.
  • Run the test for a long enough period to confirm if your hypothesis is correct or not. Document your findings and any other observations, such as changes made by competitors that may influence the outcome.
  • Take appropriate actions based on the results of your tests.

This process can be easily executed and documented by using a spreadsheet.

10. Track And Analyze KPIs

According to Roger Monti, the following are the 9 Most Important SEO KPIs to consider:

  • Customer Lifetime Value (CLV).
  • Content Efficiency.
  • Average Engagement Time.
  • Conversion Goals by Percent-Based Metrics.
  • Accurate Search Visibility.
  • Brand Visibility in Search.
  • New And Returning Users.
  • Average Time on Site.
  • Revenue Per Thousand (RPM) And Average Position.

The thing to remember about these KPIs is they are dependent upon your goals and objectives. Some may apply to your situation whereas others may not.

Think of this as a good starting point for determining how to best measure the success of a campaign.

Conclusion

Because the internet has no expiration date, mounds of information and disinformation are served up daily in various search queries.

If you aren’t careful, implementing bad or outdated advice can lead to disastrous results.

Do yourself a favor and just focus on these 10 essentials. By doing so, you will be setting yourself up for long-term success.

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Featured Image: Rawpixel.com/Shutterstock



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