How would one solve length generalization with position embeddings?

Length general ization with position embed dings refers to the problem of the model not being able to handle input sequences of different lengths . One solution to this problem is to use a technique called " padding ." P adding involves adding z eros to the end of shorter sequences to make them the same length as the longest sequence in the batch . This allows the model to process all sequences in the batch at the same time , regardless of their lengths . \n \n An other solution is to use a technique called " mask ed multi - head attention ." This involves adding a special token to the input sequence that indicates which positions are valid and which are padding . The model can then use this information to focus on the valid positions in the sequence and ignore the padding . \n \n Add itionally , using a combination of position embed dings and sequence - level embed dings can also help with length general ization . Sequ ence - level embed dings capture the overall structure of the sequence , while position embed dings capture the local context of each position . By combining these two types of embed dings , the model can better understand the relationships between different positions in the sequence and handle sequences of different lengths . </s>