GOOGLE

FLAN: Google Research Develops Better Machine Learning

Published

3 years ago

November 30, 2021

Main Article Image - Natural Language Statistics

Google recently published research on a technique to train a model to be able to solve natural language processing problems in a way that can be applied to multiple tasks. Rather than train a model to solve one kind of problem, this approach teaches it how to solve a wide range of problems, making it more efficient and advancing the state of the art.

Google Doesn’t Use All Research In Their Algorithms

Google’s official statement on research papers is that just because it publishes an algorithm doesn’t mean that it’s in use at Google Search.

Nothing in the research paper says it should be used in search. But what makes this research of interest is that it advances the state of the art and improves on current technology.

The Value Of Being Aware of Technology

People who don’t know how search engines work can end up understanding it in terms that are pure speculation.

That’s how the search industry ended up with false ideas such as “LSI Keywords” and nonsensical strategies such as trying to beat the competition by creating content that is ten times better (or simply bigger) than the competitor’s content, with zero consideration of what users might need and require.

The value in knowing about these algorithms and techniques is of being aware of the general contours of what goes on in search engines so that one does not make the error of underestimating what search engines are capable of.

The Problem That FLAN Solves

The main problem this technique solves is of enabling a machine to use its vast amount of knowledge to solve real-world tasks.

The approach teaches the machine how to generalize problem solving in order to be able to solve unseen problems.

It does this by feeding instructions to solve specific problems then generalizing those instructions in order to solve other problems.

The researchers state:

“The model is fine-tuned on disparate sets of instructions and generalizes to unseen instructions. As more types of tasks are added to the fine-tuning data model performance improves.

…We show that by training a model on these instructions it not only becomes good at solving the kinds of instructions it has seen during training but becomes good at following instructions in general.”

The research paper cites a current popular technique called “zero-shot or few-shot prompting” that trains a machine to solve a specific language problem and describes the shortcoming in this technique.

Referencing the zero shot/few shot prompting technique:

“This technique formulates a task based on text that a language model might have seen during training, where then the language model generates the answer by completing the text.

For instance, to classify the sentiment of a movie review, a language model might be given the sentence, “The movie review ‘best RomCom since Pretty Woman’ is _” and be asked to complete the sentence with either the word “positive” or “negative”.”

The researchers note that the zero shot approach performs well but that the performance has to be measured against tasks that the model has previously seen before.

The researchers write:

“…it requires careful prompt engineering to design tasks to look like data that the model has seen during training…”

And that kind of shortcoming is what FLAN solves. Because the training instructions are generalized the model is able to solve more problems including solving tasks it has not previously been trained on.

Could This Technique Be Used By Google?

Google rarely discusses specific research papers and whether or not what’s described is in use. Google’s official stance on research papers that it publishes many of them and that they don’t necessarily end up in their search ranking algorithm.

Google is generally opaque about what’s in their algorithms and rightly so.

Even when it announces new technologies Google tends to give them names that do not correspond with published research papers. For example, names like Neural Matching and Rank Brain don’t correspond with specific research papers.

It’s important to review the success of the research because some research falls short of their goals and don’t perform as well as current state of the art in techniques and algorithms.

Those research papers that fall short can more or less be ignored but they’re good to know about.

The research papers that are of most value to the search marketing community are those that are successful and perform significantly better than the current state of the art.

And that is the case with FLAN.

FLAN performs better than other techniques and for that reason FLAN is something to be aware of.

The researchers noted:

“We evaluated FLAN on 25 tasks and found that it improves over zero-shot prompting on all but four of them. We found that our results are better than zero-shot GPT-3 on 20 of 25 tasks, and better than even few-shot GPT-3 on some tasks.”

Natural Language Inference

Natural Language Inference Task is one in which the machine has to determine if a given premise is true, false or undetermined/neutral (neither true or false).

Natural Language Inference Performance of FLAN

Natural Language Inference

Reading Comprehension

This is a task of answering a question based on content in a document.

Reading Comprehension Performance of FLAN

Reading Comprehension

Closed-book QA

This is the ability to answer questions with factual data, which tests the ability to match known facts with the questions. An example is answering questions like what color is the sky or who was the first president of the United States.

Closed Book QA Performance of FLAN

Closed Book QA

Is Google Using FLAN?

As previously stated, Google does not generally confirm whether they’re using a specific algorithm or technique.

However, the fact that this particular technique moves the state of the art forward could mean that it’s not unreasonable to speculate that some form of it could be integrated into Google’s algorithm, improving its ability to answer search queries.

This research was published on October 28, 2021.

Could some of this have been incorporated into the recent Core Algorithm Update?

Core algorithm updates are generally focused on understanding queries and web pages better and providing better answers.

One can only speculate as Google rarely shares specifics, especially with regard to core algorithm updates.

Citation

Introducing FLAN: More generalizable Language Models with Instruction Fine-Tuning

Image by Shutterstock

Searchenginejournal.com

GOOGLE

Google Warns About Misuse of Its Indexing API

Published

3 days ago

September 12, 2024

Max

Google Warns About Misuse of Its Indexing API

Google has updated its Indexing API documentation with a clear warning about spam detection and the possible consequences of misuse.

Warning Against API Misuse The new message in the guide says:

“All submissions through the Indexing API are checked for spam. Any misuse, like using multiple accounts or going over the usage limits, could lead to access being taken away.”

This warning is aimed at people trying to abuse the system by exceeding the API’s limits or breaking Google’s rules.

What Is the Indexing API? The Indexing API allows websites to tell Google when job posting or livestream video pages are added or removed. It helps websites with fast-changing content get their pages crawled and indexed quickly.

But it seems some users have been trying to abuse this by using multiple accounts to get more access.

Impact of the Update Google is now closely watching how people use the Indexing API. If someone breaks the rules, they might lose access to the tool, which could make it harder for them to keep their search results updated for time-sensitive content.

How To Stay Compliant To use the Indexing API properly, follow these rules:

Don’t go over the usage limits, and if you need more, ask Google instead of using multiple accounts.
Use the API only for job postings or livestream videos, and make sure your data is correct.
Follow all of Google’s API guidelines and spam policies.
Use sitemaps along with the API, not as a replacement.

Remember, the Indexing API isn’t a shortcut to faster indexing. Follow the rules to keep your access.

GOOGLE

This Week in Search News: Simple and Easy-to-Read Update

Published

3 months ago

June 30, 2024

Entireweb News Bot

This Week in Search News: Simple and Easy-to-Read Update

Here’s what happened in the world of Google and search engines this week:

1. Google’s June 2024 Spam Update

Google finished rolling out its June 2024 spam update over a period of seven days. This update aims to reduce spammy content in search results.

2. Changes to Google Search Interface

Google has removed the continuous scroll feature for search results. Instead, it’s back to the old system of pages.

3. New Features and Tests

Link Cards: Google is testing link cards at the top of AI-generated overviews.
Health Overviews: There are more AI-generated health overviews showing up in search results.
Local Panels: Google is testing AI overviews in local information panels.

4. Search Rankings and Quality

Improving Rankings: Google said it can improve its search ranking system but will only do so on a large scale.
Measuring Quality: Google’s Elizabeth Tucker shared how they measure search quality.

5. Advice for Content Creators

Brand Names in Reviews: Google advises not to avoid mentioning brand names in review content.
Fixing 404 Pages: Google explained when it’s important to fix 404 error pages.

6. New Search Features in Google Chrome

Google Chrome for mobile devices has added several new search features to enhance user experience.

7. New Tests and Features in Google Search

Credit Card Widget: Google is testing a new widget for credit card information in search results.
Sliding Search Results: When making a new search query, the results might slide to the right.

8. Bing’s New Feature

Bing is now using AI to write “People Also Ask” questions in search results.

9. Local Search Ranking Factors

Menu items and popular times might be factors that influence local search rankings on Google.

10. Google Ads Updates

Query Matching and Brand Controls: Google Ads updated its query matching and brand controls, and advertisers are happy with these changes.
Lead Credits: Google will automate lead credits for Local Service Ads. Google says this is a good change, but some advertisers are worried.
tROAS Insights Box: Google Ads is testing a new insights box for tROAS (Target Return on Ad Spend) in Performance Max and Standard Shopping campaigns.
WordPress Tag Code: There is a new conversion code for Google Ads on WordPress sites.

These updates highlight how Google and other search engines are continuously evolving to improve user experience and provide better advertising tools.

AI

Exploring the Evolution of Language Translation: A Comparative Analysis of AI Chatbots and Google Translate

Published

7 months ago

February 26, 2024

Max

A Comparative Analysis of AI Chatbots and Google Translate

According to an article on PCMag, while Google Translate makes translating sentences into over 100 languages easy, regular users acknowledge that there’s still room for improvement.

In theory, large language models (LLMs) such as ChatGPT are expected to bring about a new era in language translation. These models consume vast amounts of text-based training data and real-time feedback from users worldwide, enabling them to quickly learn to generate coherent, human-like sentences in a wide range of languages.

However, despite the anticipation that ChatGPT would revolutionize translation, previous experiences have shown that such expectations are often inaccurate, posing challenges for translation accuracy. To put these claims to the test, PCMag conducted a blind test, asking fluent speakers of eight non-English languages to evaluate the translation results from various AI services.

The test compared ChatGPT (both the free and paid versions) to Google Translate, as well as to other competing chatbots such as Microsoft Copilot and Google Gemini. The evaluation involved comparing the translation quality for two test paragraphs across different languages, including Polish, French, Korean, Spanish, Arabic, Tagalog, and Amharic.

In the first test conducted in June 2023, participants consistently favored AI chatbots over Google Translate. ChatGPT, Google Bard (now Gemini), and Microsoft Bing outperformed Google Translate, with ChatGPT receiving the highest praise. ChatGPT demonstrated superior performance in converting colloquialisms, while Google Translate often provided literal translations that lacked cultural nuance.

For instance, ChatGPT accurately translated colloquial expressions like “blow off steam,” whereas Google Translate produced more literal translations that failed to resonate across cultures. Participants appreciated ChatGPT’s ability to maintain consistent levels of formality and its consideration of gender options in translations.

The success of AI chatbots like ChatGPT can be attributed to reinforcement learning with human feedback (RLHF), which allows these models to learn from human preferences and produce culturally appropriate translations, particularly for non-native speakers. However, it’s essential to note that while AI chatbots outperformed Google Translate, they still had limitations and occasional inaccuracies.

In a subsequent test, PCMag evaluated different versions of ChatGPT, including the free and paid versions, as well as language-specific AI agents from OpenAI’s GPTStore. The paid version of ChatGPT, known as ChatGPT Plus, consistently delivered the best translations across various languages. However, Google Translate also showed improvement, performing surprisingly well compared to previous tests.

Overall, while ChatGPT Plus emerged as the preferred choice for translation, Google Translate demonstrated notable improvement, challenging the notion that AI chatbots are always superior to traditional translation tools.

Source: https://www.pcmag.com/articles/google-translate-vs-chatgpt-which-is-the-best-language-translator