> It is difficult to see how this is an argument for the ability of LMs to demonstrate "understanding". Imagine asking a child: "how much is 4+2?" and getting a correct answer; then asking "how much is 2+4?" and getting a wrong answer. Most people would probably not take that as evidence that the second question was "wrong". They would instead conclude that the child does not "understand" addition and has only learned to reproduce specific answers to specific questions.
To use your database analogy, in what sense should we claim a database doesn't know a record when you are using a malformed SQL query? If we fixed the query and it emitted the right answer, then obviously it did store the information. The query does not encode the answer, and it is vanishingly unlikely that the database would simply accidentally return the right answer ever if it did not store the information in some way. Since LMs can get much better results just by tailoring the prompts (increased by a third in that paper! and there's no reason to think that that is the very best possible performance either!), that shows that existing practices drastically underestimate what knowledge the model has been able to learn. Learning about the real world or text is very different from learning your particular dumb broken query method.
The problem is that nobody claims that databases "know" anything. They store data. Data can be retrieved from storage. That's all they do.
>> The query does not encode the answer, and it is vanishingly unlikely that the database would simply accidentally return the right answer ever if it did not store the information in some way.
Oh, yes, absolutely. A query encodes the answer. Queries are patterns that are matched by the data stored in the database. If a query fails it's because it does not correctly represent the information it is trying to retrieve. For example, if I SELECT * FROM TABLE PEOPLE and there is no table "PEOPLE", then I don't get an answer because the query does not correctly represnt the structure of the database. You cannot retrieve any data from a database unless you have some idea about the structure of that data.
But that's not the point here. I don't disagree that a language model can learn (i.e. it can represent some elements of its training dataset). I disagree that it "understands" anything and I find the fact that it needs specific queries to retrieve the data it is representing to be evidence that it does not.
And so it's not more useful than a traditional database at this kind of task. Except it's much less precise than a traditional database and costs considerably more to create.
>> Learning about the real world or text is very different from learning your particular dumb broken query method.
I'm sorry, I don't understand what you mean here. What is my "particular dumb borken query method"? Is that meant as a personal attack?
To use your database analogy, in what sense should we claim a database doesn't know a record when you are using a malformed SQL query? If we fixed the query and it emitted the right answer, then obviously it did store the information. The query does not encode the answer, and it is vanishingly unlikely that the database would simply accidentally return the right answer ever if it did not store the information in some way. Since LMs can get much better results just by tailoring the prompts (increased by a third in that paper! and there's no reason to think that that is the very best possible performance either!), that shows that existing practices drastically underestimate what knowledge the model has been able to learn. Learning about the real world or text is very different from learning your particular dumb broken query method.