Text-to-SQL parsing, which aims at converting natural language questions into executable SQLs, has gained increasing attention in recent years.

Most of the breeds will have the first type of collapse as in Figure 5, 6, and 7. Figure 5, 6 shows the

Finally, the data was subsampled by a factor of 2. Data augmentation TX features were augmented by adding two types of artificial noise. Each session day has its own affine transform layer. RNN training hyperparameters The hyperparameters for RNN training are listed in Table 1. Table 1: RNN training hyperparameters Description Hyperparameter Learning rate 0.01 Batch size 48 Number of training batches 20000 Number of hidden units in the GRU 512 Number of GRU layers 2 Dropout rate in the GRU 0.4 Optimizer Adam Learning rate decay schedule Linear L2 weight regularization 1e-5 Maximum gradient norm for clipping 10 1.2 Language model training details Out-of-vocabulary words were mapped to a special token. In our case, T contains all 26 English letters, 5 punctuation marks, and the CTC blank symbol.

Datasets are key to all aspects of machine learning (ML) and data science, from the development of novel algorithms to training of production models.

We explore uncertainty quantification in large language models (LLMs), with the goal to identify when uncertainty in responses given a query is large. We simultaneously consider both epistemic and aleatoric uncertainties, where the former comes from the lack of knowledge about the ground truth (such as about facts or the language), and the latter comes from irreducible randomness (such as multiple possible answers).