summarize “\textbf{Data Generation\label{da}}: As the training data has already been exposed to the T5 model in the previous step for fine-tuning the initial T5 model, we cannot use the training dataset to learn the error-correction special prompt. Therefore, we will generate a new training set for prompt learning. Figure 3 illustrates such a process. 
Specifically, the data generation process (data generator) creates input-output pairs. The candidate input is generated by replacing the slot values of an input in the original training set with other values randomly sampled from the possible values for the slot. For example, "\textit{recommend(name[NAME_1 NAME_2], release\_year[1999], has\_linux\_release[yes])}" comprises the intention \textit{recommend}, slot names \textit{release\_year}, \textit{name}, and \textit{has\_linux\_release}, and slot values \textit{1999}, \textit{NAME_1 NAME_2}, and \textit{yes}. After replacement, we may obtain \textit{recommend(name[NAME_1 NAME_2], release\_year[1999], has\_linux\_release[yes])}, upon undergoing slot value replacement, yields an unseen input such as \textit{recommend(name[NAME_3], release\_year[2001], has\_linux\_release[no])}. The candidate inputs are then fed into the fine-tuned T5 model to generate initial predictions. Following the application of the \hyperref[sec:se]{Slot Error Checker}, those inputs containing slot errors are marked with error-correcting prompt. For example, in the case shown in Figure 3, we append '<token1> <token2> <token3>' next to '2001', as it is omitted from the initial prediction. The prompted inputs will be the final inputs. 

Then we need to generate ground-truth output for the newly generated inputs. To do so,  We pass the collected prompted unseen input into the fine-tuned T5 model to produce 10 predictions using beam search methods. These predictions are then subjected to \hyperref[sec:se]{Slot error checking}. The prediction that is free of slot errors is selected as the ground truth. In scenarios where beam search is employed and yields more than one output without slot errors, the output with the highest posterior probability according to the beam search is chosen.”