METHOD AND APPARATUS FOR PRE-TRAINING ARTIFICIAL INTELLIGENCE MODELS
A method for pre-training artificial intelligence models to predict a score of a user by a server, comprises: generating a first sequence for training a first model, wherein the first sequence includes a masked element related to an exercise for predicting the score of the user; inputting the first sequence to the first model to train the first model; and inputting a second sequence to a second model predicted by the first model on the basis of the first sequence to train the second model, wherein the second model is trained through comparison between the first sequence and a third sequence predicted through the second model on the basis of the second sequence.
1 . A method for pre-training artificial intelligence models to predict a score of a user by a server, comprising:
generating a first sequence for training a first model, wherein the first sequence includes a masked element related to an exercise for predicting the score of the user;
inputting the first sequence to the first model to train the first model; and
inputting a second sequence to a second model predicted by the first model on the basis of the first sequence to train the second model,
wherein the second model is trained through comparison between the first sequence and a third sequence predicted through the second model on the basis of the second sequence.
2 . The method for pre-training according to claim 1 , wherein the first sequence includes (1) an identifier of an exercise, (2) a specific part representing a type of the exercise, and (3) an element representing an answer of the user about the exercise.
3 . The method for pre-training according to claim 2 , wherein the masked element is an element representing the answer of the user about the exercise.
4 . The method for pre-training according to claim 3 , wherein the first sequence including the masked element is randomly determined on the basis of generation of a plurality of first sequences.
5 . The method for pre-training according to claim 1 , further comprising:
removing the first model;
generating a fourth sequence for fine-tuning the second model, with the second model; and
fine-tuning the second model using the fourth sequence.
6 . The method for pre-training according to claim 5 , wherein the fine-tuned second model is pre-trained to predict the score of the user.
7 . An apparatus in a server which pre-trains artificial intelligence models to predict a score of a user, comprising:
a communication module;
a memory; and
a processor,
wherein the processor generates a first sequence for training a first model, and the first sequence includes a masked element related to an exercise for predicting the score of the user,
wherein the first sequence is input to the first model to train the first model, and a second sequence predicted by the first model is input to a second model on the basis of the first sequence to train the second model, and
wherein the second model is trained through comparison between the first sequence and a third sequence predicted through the second model on the basis of the second sequence.
8 . The apparatus according to claim 7 , wherein the first sequence includes (1) an identifier of an exercise, (2) a specific part representing a type of the exercise, and (3) an element representing an answer of the user about the exercise.
9 . The apparatus according to claim 8 , wherein the masked element is an element representing the answer of the user about the exercise.
10 . The method for pre-training according to claim 6 , wherein the fine-tuned second model predicts a test score of the user on the basis of a third loss function which is the sum of a first loss function related to an output value of the first model and a second loss function related to an output value of the second model.