Google recently explores a technique called instruction fine-tuning to train a model to be able to solving natural language processing problems in a general way. 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’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.
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.
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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 main problem this…