Google AI Blog Announces KELM – An Approach That Reduces Bias And Improves Accuracy
Google’s researchers always strive to improve accuracy and reduce bias in their algorithm models. These issues led them to create KELM, or Knowledge-Enhanced Language Model Pre-training, which will undoubtedly pique the interest of many Google SEO consultants. Just recently, Google has introduced SEO consultants to this new technology in its AI Blog.
In the article, Google stated that KELM helps reduce bias and toxic content in the search engine results pages (SERPs). The technology uses a method called Text from KG Generator (TEKGEN), which can change the structured data format of Knowledge Graph facts into natural language text. With the KELM approach, researchers can improve other natural language processing models.
This means that while other models like BERT are trained on the web, KELM adds knowledge-enhanced content to the language model pre-training to reduce bias and improve factual accuracy.
Right now, Google does not fact check websites that appear in the SERPs, so if KELM launches, it will likely impact all websites that use incorrect information in their content.
With the KELM model, search engines will understand which sites are more accurate and relevant, and it could affect how sites are ranked. Businesses and webmasters will also have to improve their SEO content development and ensure that they get their facts straight, or there would be a huge drop in their rankings.
Google has yet to confirm whether or not this model is currently in use, and until the search engine company reveals this information, businesses and site owners will have no way of knowing how KELM affects SEO.
KELM Could Impact More Than Search
KELM was released under a Creative Commons license (CC BY-SA 2.0), meaning that companies like Facebook, Twitter, or Bing can better use it to better their natural language processing pre-training.
Therefore, publishers may have to adjust their content creating strategies not only in Google’s SERPs but in other search engines and social media platforms.
Indirect Ties To MUM
Google has also implied that their next-generation MUM will undergo a long and careful process, just like how they tested BERT before launching it in 2019. This is so that they can prevent bias from being introduced into their systems. Since the KELM approach reduces bias, it could be an essential asset for Google’s MUM algorithm development.
Machine Learning May Produce Biased Results
According to the research, the natural language models such as GPT-3 and BERT use data for training. However, this data could potentially cause toxic content and biases.
Professionals believe that the quality of the output is determined by the input quality. Therefore, if one trains an algorithm with high-quality data, it should also produce high-quality results. For this reason, researchers suggest that the quality of data that models like MUM and BERT use for training should be improved to remove biases.
The Knowledge Graph gathers facts in the form of structured data – a markup language that gives information that machines can easily understand. In the case of Knowledge Graph, it collects data on people, objects, and locations.
Introduced in 2012, the Google Knowledge Graph was able to help the search engine understand the relationship between things. For instance, if a searcher asks a query about Washington, the search engine will be able to distinguish if the user wants information about a person named Washington or the state.
Converting Knowledge Graph Structured Data To Natural Language Text
Google researchers said that they face problems when it comes to using knowledge base information in the training because the data is in the form of structured data. There is a need to change structured data to natural language text through data-to-text-generation. However, this data-to-text-generation can be quite a challenging task, which is why they created TEKGEN as a solution to the problem.
Researchers use TEKGEN to convert structured data into natural language text, and they use the end result – factual text – to create the KELM corpus. Moreover, they can add the additional Knowledge Graph information into the training data for better factual accuracy.
The Google AI Blog stated that KELM has real-world applications. It can be used for question answering tasks that are directly relevant to natural language processing technologies such as MUM and BERT, as well as information retrieval like search.
Google is known for conducting a lot of research, and some of its algorithms delve into what seemed impossible tasks. Some of the company’s research is never used in its algorithm systems, and the research papers usually conclude with a statement that says “more research” is needed as the model in question does not live up to expectations.
However, this is not how they represented KELM and TEKGEN. Google AI blog showed optimism about these technologies being used in practical applications. Moreover, there is a huge possibility that KELM would eventually make it in their algorithm systems.
The KELM corpus is significant to Google’s recent announcement – the MUM algorithm – which requires factual accuracy. But KELM’s application is not limited to the MUM algorithm.
The fact that Google is creating a model that can reduce bias and improve factual accuracy is big news for both SEOs and users. Google is nevertheless optimistic about their new research, and there is a good chance that KELM will be used to better search in the future.
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