input correctly by setting T^ = and ^ = .

[rifattryo.ut11]

No additional transformations are applied to t+ before ending this layer. This process is repeated until the final layer where the final text output is read from T_. The final output is converted to t through the prediction head generated by the last layer and supervised by standard next t prediction. T is first pre-trained before fine-tuning to enable robust execution of the -covered thirty algorithms. This approach is known to achieve out-of-distribution generalization in graph space up to times the input size.


However, each problem has a clear polynomial time iran phone number example solution, which means that the number of parameters of a typical M today should be sufficient to solve these problems. The dataset contains 10,000 samples of each input size for each algorithm, totaling 10,000 data points, which are used for training and validation. Training details The experiment sets the size of th to h and uses the m optimizer with a learning rate of -. As mentioned above, random position encodings of maximum length are applied on top of the rotational position encodings of all hhm and are kept frozen during training.



Evaluation Metrics The authors propose that suitable evaluation metrics should reflect the reasons why the model fails on a specific sample and require a metric of how close the output is to the correct answer. Therefore, it is absolutely infeasible to use exact string matching to calculate model accuracy. The performance metrics chosen by the paper include the following three. Shape score A binary metric that determines whether the output has the correct shape. For example, in a sorting task, the output should have exactly the same number of elements as the input. Or if the output is a matrix, we need to ensure that its shape is consistent with the input and the task.



Parsing score A binary metric that determines whether the output does not contain any illegal characters. For example, in a task of sorting a list of numbers, the output should not contain any letters. The score is the percentage of elements in the output that match the true answer, which is also commonly used for -testing. The score is automatically reset to zero when the shape score is 0. This multi-faceted metric design is able to capture various failure modes of M for reasoning tasks on text. For example, over-specialization training on a certain problem size may result in incorrect output shape.