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How to Rank Higher on Google (10 Steps)

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How to Rank Higher on Google (10 Steps)

Hundreds of factors could improve your Google rankings. But some are more difficult to influence than others.

For that reason, if you want to rank higher, you need to be methodical. Start by working on the easy things that are within your control, then move on to more challenging things if needed.

Here’s the process:

How to rank higher on Google

Before we get started…

The process in this guide works best for internal pages. If you want to rank your homepage, read our guide to homepage SEO. If you run a local business and want to rank in local search, read our guide to local SEO.

If you already know which keyword you want to rank higher for, skip this step. Otherwise, you need to find a page that could rank higher for its target keyword. 

Here’s an easy way to do this:

  1. Paste your domain into Ahrefs’ Site Explorer
  2. Go to the Organic keywords report
  3. Filter for rankings in positions 2–10

You should now see all keywords you rank for on the first page of Google, but not in position #1. All you have to do is choose one.

Underperforming keyword rankings on the first page of Google, via Ahrefs' Site Explorer

Here’s a tip: look for your main keyword targets for the corresponding page.

For example, our guide to learning SEO ranks in position #10 for “how long does it take to learn SEO.” But this isn’t the primary target keyword for the page, so it’s likely not the best one to try to improve rankings for.

Example of a long-tail keyword that probably isn't worth trying to improve rankings for

Recommendation

If you don’t have any first-page rankings, try to rank higher for keywords on page two or three. It’s usually easier to improve rankings for pages that are already doing quite well. But if you don’t have any, improving rankings on page two or three is your best bet. 

Search intent is the “why” behind the query. It’s why 90% of the top-ranking results for “air fryer” are blog posts and not sales pages. Google understands that searchers aren’t ready to buy. They want to compare products.

People searching for "air fryer" are looking to compare products, not buy

You need to align your page with search intent to stand the best chance at ranking.

We learned this the hard way when trying to rank for “backlink checker.” 

Here’s the page we first created: 

Our original backlink checker page didn't match search intent

You can see that it explains how to check a site’s backlinks with Ahrefs and offers searchers a free trial. 

This performed OK and ranked for years in positions #6–10—but it never cracked the top five.

In 2018, we realized this was a search intent issue. All the top-ranking pages for “backlink checker” were free tools. 

The top-ranking results for "backlink checker" in 2018 were all free tools

To solve this, we added a free tool to the page. Almost overnight, the page shot to #1—and it’s been there ever since.

Our rankings for "backlink checker" over time, via Ahrefs' Rank Tracker

To see how well your page aligns with intent, check the top-ranking pages for the three Cs:

  1. Content type Are they mainly blog posts, product, category, landing pages, or something else? 
  2. Content format Are they mainly tutorials, listicles, how-to guides, recipes, free tools, or something else?
  3. Content angle – Is there a dominant selling point, like low prices or how easy it is?

For example, all the top-ranking pages for “pancake recipe” are blog posts with recipes. And the dominant selling point is how easy they are to make.

People searching for "pancake recipe" clearly want a blog post with an easy recipe

3. Cover the topic in full

Even if your content aligns with search intent, you may not be giving searchers everything they want. There may be subtopics they’re looking for and expecting you to cover.

For example, most top-ranking results for “how to write a press release” are how-to blog posts. This paints a clear picture of search intent.

People searching for "how to write a press release" clearly want a step-by-step guide

But if you look at these posts, most of them include a template or links to templates.

Example of a page ranking for "how to write a press release" with a free template
Example of another page ranking for "how to write a press release" with a free template

Google likely knows that searchers value posts with templates more than those without, so you should probably include one if you want to rank higher for this keyword.

Here are a couple of ways to find subtopics to include in your content:

  1. Eyeball the top-ranking pages for commonalities – Pay particular attention to subheadings. 
  2. Find keywords top-ranking pages rank for that you don’t – These often map to subtopics. 

Here’s how to do the latter in Ahrefs’ Site Explorer:

  1. Paste your page into Site Explorer
  2. Go to the Content Gap report
  3. Paste in a few top-ranking URLs

For example, here’s what we get if we plug in our post about creating a go-to-market strategy

Example of subtopics searchers want to see

Freshness is a query-dependent Google ranking factor. If searchers are likely to value updated content, Google ranks fresh pages higher. 

For example, people value freshness when searching for “top google searches.” They want the most popular Google searches right now, not 10 years ago. That’s why rankings and traffic for our page drop when the content becomes stale and jump back up when we update the page.

Keyword ranking fluctuations for our list of top Google searches over time

If you’re unsure whether Google values freshness, check the dates on top-ranking pages. 

For example, all top-ranking results for “best headphones” were updated recently. But many top-ranking results for “best parks in london” haven’t been updated for months or even years. This doesn’t matter because it’s not like new parks are built daily.

The results for "best headphones" are all fresh
The results for "best parks in london" aren't particularly fresh

If freshness is important for your keyword, you may rank higher by refreshing your page

Making the purpose and relevance of your page clear to Google and searchers is the job of on-page SEO. It’s the icing on the cake that highlights the work you put into matching intent and covering the topic in full.

Here are a few simple ways to improve your on-page SEO:

  • Use H1–H6 tags to structure your content hierarchically Google recommends this. Wrap your title in an H1, subheadings in H2s, sub-subheadings in H3s, etc.
  • Use a short, descriptive URL Google says simple URLs convey content information. 
  • Write a compelling title tag and meta description – This may help you get more clicks and send positive signals about your content to Google.
  • Optimize your images – Google says to use brief but descriptive filenames and alt text. It’s also worth compressing images to improve page speed—which is a ranking factor.
  • Polish your copy – Google says users enjoy content that’s well written and easy to follow. Use short paragraphs, good grammar, and proven copywriting techniques to keep readers engaged.

Learn more: On-Page SEO: Complete Beginner’s Guide

Internal links are links from one page on your website to another. They’re important because they’re how PageRank flows around your site. In other words, internal links boost a page’s authority and tell Google it’s important.

Here’s an easy way to find relevant internal link opportunities:

  1. Sign up for a free Ahrefs Webmaster Tools account
  2. Crawl your website with Site Audit
  3. Go to the Internal link opportunities tool
  4. Set the target URL to the page you want to rank higher

This tool takes all the keywords your target page ranks for in the top 100 and finds mentions of them on your site. It then suggests them as contextual internal link opportunities.

For example, suppose we set our guide to building a content marketing strategy as the target page. In that case, there are 13 potential internal link opportunities. 

Searching for internal link opportunities in Ahrefs' Site Audit

Here’s one of them:

Example of an internal link opportunity, via Ahrefs' Site Audit

Here, it’s suggesting that we internally link the phrase “content marketing strategy” in our list of content marketing tools.

Internally linking this phrase may help our post to rank higher. 

If you don’t see any results in this report, it’s either because:

  1. Your target page doesn’t rank in the top 100 for any keywords.
  2. You don’t mention any of the keywords it ranks for on your site.

Either way, you can find opportunities in Google by searching for site:domain.com "".

For example, there are plenty of mentions of “content marketing strategy” on our blog.

Searching Google for internal link opportunities

Having many similar pages about the same thing is shooting yourself in the foot. That’s because backlinks get spread between pages and are a ranking factor. So you can end up with many weak pages instead of one strong enough to rank.

One strong page about a topic will usually rank higher than multiple weak pages about that topic

To solve this problem, redirect weaker pages about a topic to the strongest one. This consolidates backlinks and creates a page more capable of ranking.

Redirecting and consolidating pages about the same thing gives you a strong page more capable of ranking

To find pages on your site about the keyword you want to rank higher for, search Google for site:domain.com .

For example, our site has two very similar pages about meta keywords. We have a blog post explaining meta keywords and a glossary page that does the same thing. 

Example of a possible keyword cannibalization issue

Redirecting one of these to the other to consolidate backlinks may help us rank higher for this keyword. It creates one stronger page from two weaker pages.

Recommendation

Keep search intent in mind when doing this. If your pages fulfill different intents, redirecting may not be the best idea. This tactic is best when pages are very similar. 

Backlinks are one of Google’s most important ranking factors. Andrey Lipattsev, a search quality senior strategist at Google, confirmed this in 2016. 

Unfortunately, building high-quality backlinks is one of the most challenging parts of SEO. This is because it’s not something you can fully control. You have to create something worthy of earning backlinks, then convince people to link to you. 

This is why it’s the final step in our process. 

Here’s a good starting point if you’re new to link building:

  1. Paste a competing page into Site Explorer
  2. Go to the Backlinks report
  3. Look for backlinks you may be able to replicate

For example, let’s say we want to build links to our beginner’s guide to SEO. If we search Google for competing pages with the Ahrefs’ SEO Toolbar installed, we’ll find that one has links from over 14K referring domains.

Number of referring domains to Moz's beginner's guide to SEO, via Ahrefs' SEO Toolbar

According to Ahrefs’ Site Explorer, here’s one of the pages it got a backlink from recently:

Example of a potentially replicable backlink via the Backlinks report in Ahrefs' Site Explorer

Looking at the referring page, it seems the link comes from a section listing three of the best SEO guides. 

Example of page linking to competing beginners' guides

If we reach out to the author and introduce our beginner’s guide to SEO, there’s a chance they may add it to their page.

Rank tracking is the only way to know if your efforts to rank higher on Google are working. 

Although you can do this for free by searching on Google, it isn’t usually reliable. This is because factors like location and search history can affect where you see a page ranking. Using a rank tracking tool like Ahrefs’ Rank Tracker is much more accurate.

You can track 10K keywords in this tool, but you only usually need to track the main keyword for each page. 

Hit the graph caret next to any keyword in Rank Tracker to see its ranking progress over time. 

Rank tracking in Ahrefs' Rank Tracker

Ranking high on Google for one keyword is great, but ranking high for many keywords is even better. So once you’ve followed this process for one keyword, repeat it for more.

You’ll rank higher for hundreds of keywords and get tons of organic traffic before you know it.

Final thoughts

You can do many things to rank higher on Google, but it makes sense to start with the easy ones that are within your control. If those don’t move the needle, invest in more challenging things like link building.

Give me a shout on Twitter if you have any questions. 



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Optimize Your SEO Strategy For Maximum ROI With These 5 Tips

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Optimize Your SEO Strategy For Maximum ROI With These 5 Tips

Wondering what improvements can you make to boost organic search results and increase ROI?

If you want to be successful in SEO, even after large Google algorithm updates, be sure to:

  1. Keep the SEO fundamentals at the forefront of your strategy.
  2. Prioritize your SEO efforts for the most rewarding outcomes.
  3. Focus on uncovering and prioritizing commercial opportunities if you’re in ecommerce.
  4. Dive into seasonal trends and how to plan for them.
  5. Get tip 5 and all of the step-by-step how-tos by joining our upcoming webinar.

We’ll share five actionable ways you can discover the most impactful opportunities for your business and achieve maximum ROI.

You’ll learn how to:

  • Identify seasonal trends and plan for them.
  • Report on and optimize your online share of voice.
  • Maximize SERP feature opportunities, most notably Popular Products.

Join Jon Earnshaw, Chief Product Evangelist and Co-Founder of Pi Datametrics, and Sophie Moule, Head of Product and Marketing at Pi Datametrics, as they walk you through ways to drastically improve the ROI of your SEO strategy.

In this live session, we’ll uncover innovative ways you can step up your search strategy and outperform your competitors.

Ready to start maximizing your results and growing your business?

Sign up now and get the actionable insights you need for SEO success.

Can’t attend the live webinar? We’ve got you covered. Register anyway and you’ll get access to a recording, after the event.



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TikTok’s US Future Uncertain: CEO Faces Congress

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TikTok's US Future Uncertain: CEO Faces Congress

During a five-hour congressional hearing, TikTok CEO Shou Zi Chew faced intense scrutiny from U.S. lawmakers about the social media platform’s connections to its Chinese parent company, ByteDance.

Legislators from both sides demanded clear answers on whether TikTok spies on Americans for China.

The U.S. government has been pushing for the divestiture of TikTok and has even threatened to ban the app in the United States.

Chew found himself in a difficult position, attempting to portray TikTok as an independent company not influenced by China.

However, lawmakers remained skeptical, citing China’s opposition to the sale of TikTok as evidence of the country’s influence over the company.

The hearing was marked by a rare display of bipartisan unity, with the tone harsher than in previous congressional hearings featuring American social media executives.

The Future of TikTok In The US

With the U.S. and China at odds over TikTok’s sale, the app faces two possible outcomes in the United States.

Either TikTok gets banned, or it revisits negotiations for a technical fix to data security concerns.

Lindsay Gorman, head of technology and geopolitics at the German Marshall Fund, said, “The future of TikTok in the U.S. is definitely dimmer and more uncertain today than it was yesterday.”

TikTok has proposed measures to protect U.S. user data, but no security agreement has been reached.

Addressing Concerns About Societal Impact

Lawmakers at the hearing raised concerns about TikTok’s impact on young Americans, accusing the platform of invading privacy and harming mental health.

According to the Pew Research Center, the app is used by 67% of U.S. teenagers.

Critics argue that the app is too addictive and its algorithm can expose teens to dangerous or lethal situations.

Chew pointed to new screen time limits and content guidelines to address these concerns, but lawmakers remained unconvinced.

In Summary

The House Energy and Commerce Committee’s hearing on TikTok addressed concerns common to all social media platforms, like spreading harmful content and collecting massive user data.

Most committee members were critical of TikTok, but many avoided the typical grandstanding seen in high-profile hearings.

The hearing aimed to make a case for regulating social media and protecting children rather than focusing on the national security threat posed by the app’s connection to China.

If anything emerges from this hearing, it could be related to those regulations.

The hearing also allowed Congress to convince Americans that TikTok is a national security threat that warrants a ban.

This concern arises from the potential for the Chinese government to access the data of TikTok’s 150 million U.S. users or manipulate its recommendation algorithms to spread propaganda or disinformation.

However, limited public evidence supports these claims, making banning the app seem extreme and potentially unnecessary.

As events progress, staying informed is crucial as the outcome could impact the digital marketing landscape.


Featured Image: Rokas Tenys/Shutterstock

Full replay of congressional hearing available on YouTube.



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Everything You Need To Know

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Everything You Need To Know

Google has just released Bard, its answer to ChatGPT, and users are getting to know it to see how it compares to OpenAI’s artificial intelligence-powered chatbot.

The name ‘Bard’ is purely marketing-driven, as there are no algorithms named Bard, but we do know that the chatbot is powered by LaMDA.

Here is everything we know about Bard so far and some interesting research that may offer an idea of the kind of algorithms that may power Bard.

What Is Google Bard?

Bard is an experimental Google chatbot that is powered by the LaMDA large language model.

It’s a generative AI that accepts prompts and performs text-based tasks like providing answers and summaries and creating various forms of content.

Bard also assists in exploring topics by summarizing information found on the internet and providing links for exploring websites with more information.

Why Did Google Release Bard?

Google released Bard after the wildly successful launch of OpenAI’s ChatGPT, which created the perception that Google was falling behind technologically.

ChatGPT was perceived as a revolutionary technology with the potential to disrupt the search industry and shift the balance of power away from Google search and the lucrative search advertising business.

On December 21, 2022, three weeks after the launch of ChatGPT, the New York Times reported that Google had declared a “code red” to quickly define its response to the threat posed to its business model.

Forty-seven days after the code red strategy adjustment, Google announced the launch of Bard on February 6, 2023.

What Was The Issue With Google Bard?

The announcement of Bard was a stunning failure because the demo that was meant to showcase Google’s chatbot AI contained a factual error.

The inaccuracy of Google’s AI turned what was meant to be a triumphant return to form into a humbling pie in the face.

Google’s shares subsequently lost a hundred billion dollars in market value in a single day, reflecting a loss of confidence in Google’s ability to navigate the looming era of AI.

How Does Google Bard Work?

Bard is powered by a “lightweight” version of LaMDA.

LaMDA is a large language model that is trained on datasets consisting of public dialogue and web data.

There are two important factors related to the training described in the associated research paper, which you can download as a PDF here: LaMDA: Language Models for Dialog Applications (read the abstract here).

  • A. Safety: The model achieves a level of safety by tuning it with data that was annotated by crowd workers.
  • B. Groundedness: LaMDA grounds itself factually with external knowledge sources (through information retrieval, which is search).

The LaMDA research paper states:

“…factual grounding, involves enabling the model to consult external knowledge sources, such as an information retrieval system, a language translator, and a calculator.

We quantify factuality using a groundedness metric, and we find that our approach enables the model to generate responses grounded in known sources, rather than responses that merely sound plausible.”

Google used three metrics to evaluate the LaMDA outputs:

  1. Sensibleness: A measurement of whether an answer makes sense or not.
  2. Specificity: Measures if the answer is the opposite of generic/vague or contextually specific.
  3. Interestingness: This metric measures if LaMDA’s answers are insightful or inspire curiosity.

All three metrics were judged by crowdsourced raters, and that data was fed back into the machine to keep improving it.

The LaMDA research paper concludes by stating that crowdsourced reviews and the system’s ability to fact-check with a search engine were useful techniques.

Google’s researchers wrote:

“We find that crowd-annotated data is an effective tool for driving significant additional gains.

We also find that calling external APIs (such as an information retrieval system) offers a path towards significantly improving groundedness, which we define as the extent to which a generated response contains claims that can be referenced and checked against a known source.”

How Is Google Planning To Use Bard In Search?

The future of Bard is currently envisioned as a feature in search.

Google’s announcement in February was insufficiently specific on how Bard would be implemented.

The key details were buried in a single paragraph close to the end of the blog announcement of Bard, where it was described as an AI feature in search.

That lack of clarity fueled the perception that Bard would be integrated into search, which was never the case.

Google’s February 2023 announcement of Bard states that Google will at some point integrate AI features into search:

“Soon, you’ll see AI-powered features in Search that distill complex information and multiple perspectives into easy-to-digest formats, so you can quickly understand the big picture and learn more from the web: whether that’s seeking out additional perspectives, like blogs from people who play both piano and guitar, or going deeper on a related topic, like steps to get started as a beginner.

These new AI features will begin rolling out on Google Search soon.”

It’s clear that Bard is not search. Rather, it is intended to be a feature in search and not a replacement for search.

What Is A Search Feature?

A feature is something like Google’s Knowledge Panel, which provides knowledge information about notable people, places, and things.

Google’s “How Search Works” webpage about features explains:

“Google’s search features ensure that you get the right information at the right time in the format that’s most useful to your query.

Sometimes it’s a webpage, and sometimes it’s real-world information like a map or inventory at a local store.”

In an internal meeting at Google (reported by CNBC), employees questioned the use of Bard in search.

One employee pointed out that large language models like ChatGPT and Bard are not fact-based sources of information.

The Google employee asked:

“Why do we think the big first application should be search, which at its heart is about finding true information?”

Jack Krawczyk, the product lead for Google Bard, answered:

“I just want to be very clear: Bard is not search.”

At the same internal event, Google’s Vice President of Engineering for Search, Elizabeth Reid, reiterated that Bard is not search.

She said:

“Bard is really separate from search…”

What we can confidently conclude is that Bard is not a new iteration of Google search. It is a feature.

Bard Is An Interactive Method For Exploring Topics

Google’s announcement of Bard was fairly explicit that Bard is not search. This means that, while search surfaces links to answers, Bard helps users investigate knowledge.

The announcement explains:

“When people think of Google, they often think of turning to us for quick factual answers, like ‘how many keys does a piano have?’

But increasingly, people are turning to Google for deeper insights and understanding – like, ‘is the piano or guitar easier to learn, and how much practice does each need?’

Learning about a topic like this can take a lot of effort to figure out what you really need to know, and people often want to explore a diverse range of opinions or perspectives.”

It may be helpful to think of Bard as an interactive method for accessing knowledge about topics.

Bard Samples Web Information

The problem with large language models is that they mimic answers, which can lead to factual errors.

The researchers who created LaMDA state that approaches like increasing the size of the model can help it gain more factual information.

But they noted that this approach fails in areas where facts are constantly changing during the course of time, which researchers refer to as the “temporal generalization problem.”

Freshness in the sense of timely information cannot be trained with a static language model.

The solution that LaMDA pursued was to query information retrieval systems. An information retrieval system is a search engine, so LaMDA checks search results.

This feature from LaMDA appears to be a feature of Bard.

The Google Bard announcement explains:

“Bard seeks to combine the breadth of the world’s knowledge with the power, intelligence, and creativity of our large language models.

It draws on information from the web to provide fresh, high-quality responses.”

Screenshot of a Google Bard Chat, March 2023

LaMDA and (possibly by extension) Bard achieve this with what is called the toolset (TS).

The toolset is explained in the LaMDA researcher paper:

“We create a toolset (TS) that includes an information retrieval system, a calculator, and a translator.

TS takes a single string as input and outputs a list of one or more strings. Each tool in TS expects a string and returns a list of strings.

For example, the calculator takes “135+7721”, and outputs a list containing [“7856”]. Similarly, the translator can take “hello in French” and output [‘Bonjour’].

Finally, the information retrieval system can take ‘How old is Rafael Nadal?’, and output [‘Rafael Nadal / Age / 35’].

The information retrieval system is also capable of returning snippets of content from the open web, with their corresponding URLs.

The TS tries an input string on all of its tools, and produces a final output list of strings by concatenating the output lists from every tool in the following order: calculator, translator, and information retrieval system.

A tool will return an empty list of results if it can’t parse the input (e.g., the calculator cannot parse ‘How old is Rafael Nadal?’), and therefore does not contribute to the final output list.”

Here’s a Bard response with a snippet from the open web:

Google Bard: Everything You Need To KnowScreenshot of a Google Bard Chat, March 2023

Conversational Question-Answering Systems

There are no research papers that mention the name “Bard.”

However, there is quite a bit of recent research related to AI, including by scientists associated with LaMDA, that may have an impact on Bard.

The following doesn’t claim that Google is using these algorithms. We can’t say for certain that any of these technologies are used in Bard.

The value in knowing about these research papers is in knowing what is possible.

The following are algorithms relevant to AI-based question-answering systems.

One of the authors of LaMDA worked on a project that’s about creating training data for a conversational information retrieval system.

You can download the 2022 research paper as a PDF here: Dialog Inpainting: Turning Documents into Dialogs (and read the abstract here).

The problem with training a system like Bard is that question-and-answer datasets (like datasets comprised of questions and answers found on Reddit) are limited to how people on Reddit behave.

It doesn’t encompass how people outside of that environment behave and the kinds of questions they would ask, and what the correct answers to those questions would be.

The researchers explored creating a system read webpages, then used a “dialog inpainter” to predict what questions would be answered by any given passage within what the machine was reading.

A passage in a trustworthy Wikipedia webpage that says, “The sky is blue,” could be turned into the question, “What color is the sky?”

The researchers created their own dataset of questions and answers using Wikipedia and other webpages. They called the datasets WikiDialog and WebDialog.

  • WikiDialog is a set of questions and answers derived from Wikipedia data.
  • WebDialog is a dataset derived from webpage dialog on the internet.

These new datasets are 1,000 times larger than existing datasets. The importance of that is it gives conversational language models an opportunity to learn more.

The researchers reported that this new dataset helped to improve conversational question-answering systems by over 40%.

The research paper describes the success of this approach:

“Importantly, we find that our inpainted datasets are powerful sources of training data for ConvQA systems…

When used to pre-train standard retriever and reranker architectures, they advance state-of-the-art across three different ConvQA retrieval benchmarks (QRECC, OR-QUAC, TREC-CAST), delivering up to 40% relative gains on standard evaluation metrics…

Remarkably, we find that just pre-training on WikiDialog enables strong zero-shot retrieval performance—up to 95% of a finetuned retriever’s performance—without using any in-domain ConvQA data. “

Is it possible that Google Bard was trained using the WikiDialog and WebDialog datasets?

It’s difficult to imagine a scenario where Google would pass on training a conversational AI on a dataset that is over 1,000 times larger.

But we don’t know for certain because Google doesn’t often comment on its underlying technologies in detail, except on rare occasions like for Bard or LaMDA.

Large Language Models That Link To Sources

Google recently published an interesting research paper about a way to make large language models cite the sources for their information. The initial version of the paper was published in December 2022, and the second version was updated in February 2023.

This technology is referred to as experimental as of December 2022.

You can download the PDF of the paper here: Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models (read the Google abstract here).

The research paper states the intent of the technology:

“Large language models (LLMs) have shown impressive results while requiring little or no direct supervision.

Further, there is mounting evidence that LLMs may have potential in information-seeking scenarios.

We believe the ability of an LLM to attribute the text that it generates is likely to be crucial in this setting.

We formulate and study Attributed QA as a key first step in the development of attributed LLMs.

We propose a reproducible evaluation framework for the task and benchmark a broad set of architectures.

We take human annotations as a gold standard and show that a correlated automatic metric is suitable for development.

Our experimental work gives concrete answers to two key questions (How to measure attribution?, and How well do current state-of-the-art methods perform on attribution?), and give some hints as to how to address a third (How to build LLMs with attribution?).”

This kind of large language model can train a system that can answer with supporting documentation that, theoretically, assures that the response is based on something.

The research paper explains:

“To explore these questions, we propose Attributed Question Answering (QA). In our formulation, the input to the model/system is a question, and the output is an (answer, attribution) pair where answer is an answer string, and attribution is a pointer into a fixed corpus, e.g., of paragraphs.

The returned attribution should give supporting evidence for the answer.”

This technology is specifically for question-answering tasks.

The goal is to create better answers – something that Google would understandably want for Bard.

  • Attribution allows users and developers to assess the “trustworthiness and nuance” of the answers.
  • Attribution allows developers to quickly review the quality of the answers since the sources are provided.

One interesting note is a new technology called AutoAIS that strongly correlates with human raters.

In other words, this technology can automate the work of human raters and scale the process of rating the answers given by a large language model (like Bard).

The researchers share:

“We consider human rating to be the gold standard for system evaluation, but find that AutoAIS correlates well with human judgment at the system level, offering promise as a development metric where human rating is infeasible, or even as a noisy training signal. “

This technology is experimental; it’s probably not in use. But it does show one of the directions that Google is exploring for producing trustworthy answers.

Research Paper On Editing Responses For Factuality

Lastly, there’s a remarkable technology developed at Cornell University (also dating from the end of 2022) that explores a different way to source attribution for what a large language model outputs and can even edit an answer to correct itself.

Cornell University (like Stanford University) licenses technology related to search and other areas, earning millions of dollars per year.

It’s good to keep up with university research because it shows what is possible and what is cutting-edge.

You can download a PDF of the paper here: RARR: Researching and Revising What Language Models Say, Using Language Models (and read the abstract here).

The abstract explains the technology:

“Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog.

However, they sometimes generate unsupported or misleading content.

A user cannot easily determine whether their outputs are trustworthy or not, because most LMs do not have any built-in mechanism for attribution to external evidence.

To enable attribution while still preserving all the powerful advantages of recent generation models, we propose RARR (Retrofit Attribution using Research and Revision), a system that 1) automatically finds attribution for the output of any text generation model and 2) post-edits the output to fix unsupported content while preserving the original output as much as possible.

…we find that RARR significantly improves attribution while otherwise preserving the original input to a much greater degree than previously explored edit models.

Furthermore, the implementation of RARR requires only a handful of training examples, a large language model, and standard web search.”

How Do I Get Access To Google Bard?

Google is currently accepting new users to test Bard, which is currently labeled as experimental. Google is rolling out access for Bard here.

Google Bard is ExperimentalScreenshot from bard.google.com, March 2023

Google is on the record saying that Bard is not search, which should reassure those who feel anxiety about the dawn of AI.

We are at a turning point that is unlike any we’ve seen in, perhaps, a decade.

Understanding Bard is helpful to anyone who publishes on the web or practices SEO because it’s helpful to know the limits of what is possible and the future of what can be achieved.

More Resources:


Featured Image: Whyredphotographor/Shutterstock



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