FLAN Improves Machine Learning for NLP Problems

Google recently published an SEO research paper on training a model called Fine-tuned LAnguage Net (FLAN) is used to solve natural language processing (NLP) problems in a manner that can be used for many tasks. Instead of teaching a model how to deal with one problem at a time, this method teaches it how to address a wide range of issues for greater effectiveness and efficiency. Taking an in-depth look into this research paper may prove beneficial for SEO experts as it provides insights into how Google may tweak its algorithms in the future.

Google Claims It Doesn’t Use All Research in Their Algorithms

Google made an official statement on research papers, saying that not all published research papers are used in their Google Search algorithms. The research paper also did not say that it would be used in the search results. However, the research is still worth noting because it improves the current SEO technology and advances the state of the art.

Benefits of Being Aware of Technology

People unfamiliar with search engines may arrive at incorrect conclusions based on their assumptions. This is why many in the SEO community continue to employ ineffective tactics without considering what users want and require. Being knowledgeable about these algorithms and methods might help one avoid making the mistake of underestimating the capability of Google’s search engine.

What Does FLAN Solve?

The major advantage of this technique is that it allows machines to apply their massive amount of knowledge to address real-world problems. The method teaches the machine how to generalise problem-solving to address unforeseen issues.

It feeds the machine instructions on solving specific issues and generalises those instructions to address other problems. According to the researchers, the model gets better at following instructions it has encountered during training, as well as all kinds of instructions in general.

The SEO research paper also mentioned the “zero-shot or few-shot prompting” technique that trains machines to solve a specific language issue and describes its shortcomings. The researchers said that this technique formulates a task depending on the language model’s text during training, where the model generates the answer by completing the text.

For example, the language model might be presented with a sentence to classify a movie review’s sentiment. Suppose the sentence is: “the movie review ‘best horror since Amityville’ is ___”. It will then ask the language model to complete the sentence with either “positive” or “negative”.

This zero-shot method performs well, but researchers should first measure its performance on tasks that the model has encountered before. According to the researchers, they need to conduct careful prompt engineering to design tasks similar to the data the model encountered during training.

FLAN was created to solve this kind of shortcoming. Since the training instructions are generalised, the model can solve additional issues, including those it has not seen during training.

Google on Using This Technique

Google rarely talks about specific research papers and whether or not the findings are implemented. The company’s official stance on its academic papers is that many do not necessarily end up in their search ranking algorithm. They’re unclear about what is included in their algorithms, which is fair.

Even when the search engine company reveals new technologies, Google usually gives them names that don’t correspond to published research papers. For instance, the names Rank Brain and Neural Matching do not match any existing studies. One must assess the research’s success since some studies may not be able to achieve their objectives and are not as good as cutting-edge algorithms and techniques. Although these research papers fall short, it is still good to know about them.

The most useful research papers are successful and outperform the existing state of the art. And, as it happens, FLAN is one of them. FLAN outshines other methods in terms of performance and, as a result, deserves to be taken notice of.

The researchers tested FLAN on 25 tasks and discovered that it increases performance over zero-shot prompting on all four of them. On 20 of the 25 tasks, they observed superior results than zero-shot GPT-3 and even few-shot GPT-3.

Reading Comprehension

Reading comprehension refers to answering queries based on content in a document. FLAN’s reading comprehension performance is 77.4, while GPT-3 Few-Shot and GPT-3 Zero-Shot are 72.6 and 63.7, respectively.

Natural Language Inference

With natural language inference tasks, the machine needs to distinguish whether a given premise is true, false, or neither of the two (neutral/undetermined).

FLAN’s natural language inference is 56.2, while GPT-3 Few-Shot and GPT-3 Zero-Shot are 53.2 and 42.9, respectively.

Closed-book QA

This refers to the ability to answer queries using factual information, which tests the ability to pair facts with questions. For instance, it helps answer questions like, “who was the first president of the United States” or “what colour is the sky”?

FLAN’s closed book QA performance is 56.6, while GPT-3 Few-Shot and GPT-3 Zero-Shot are 55.7 and 49.8, respectively.

FLAN in Google’s Algorithms

As previously said, Google does not generally confirm whether or not they are employing a certain technique or algorithm. However, the fact that this method enables state of the art to advance might suggest that it will be included in Google’s algorithm in some way, boosting its capability to answer search queries.

This paper was published on 28 October 2021.

Some people think this research must have been incorporated into the recent core algorithm update. These updates usually focus on understanding web pages and questions to provide users with better answers. However, Google never announces the techniques they use in their algorithms, so one can only speculate if FLAN is already implemented in the latest SEO algorithm update.

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