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:content {:text "e explore AI models for PBNL and integrate those models with the visual tool."},
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:content {:text "such models heavily rely on data to learn such behaviour and would need to be trained on an aligned dataset of natural language queries and the corresponding sklearn line(s) of code demonstrating how a user would want to use such an intelligent search too"},
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:content {:text " We further filter out notebooks with non-English descriptions in all of the markdown cells, resulting in 59,569 notebooks"},
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:content {:text ". A common alternative is to pretrain a larger model on a large dataset based on a generic task, such as masked token infilling, and/or a combination of objectives. Such models can then be fine-tuned (calibrated) on a smaller dataset to high accuracy. For the second paradigm, we fine-tune one such“medium” sized model, CodeT5 [10]. Finally, Large Language Models (LLMs), with billions of parameters, are often able to generalize to new tasks from just a few examples. These models are typically expensive to train from scratch or fine-tune; we adopt such a model, named CodeGen [11], and query it by means of few-shot prompting. We elaborate on these models below. Note that we use top-k sampling for our top-5 results.(A comparison of results with other decoding strategies can be found in Section 3 and 4 in the supplementary material"},
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:content {:text ") Experimental Setup"},
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:content {:text "We log all the NL queries that users searched for in LOWCODER during the user studie"},
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:content {:text "We evaluate the models’ ability to generate just the operator name as well as the entire operator invocation (including all the hyper-parameter names and values) based on the hybrid formulation"},
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:content {:text " Test Metrics"},
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:content {:text "e compare the model’s ability to correctly generate the operator name and the operator invocation based on the formulation corresponding to the training task using top-1 and top-5 accuracy as shown in Figure 4"},
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:content {:text "(See Section 4 in the supplementary material for additional results and ablation studies.)"},
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:content {:text " 141 out of185 queries correctly for an overall accuracy of 76.2%."},
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:content {:text " participants were instructed to create AI pipelines with data preprocessing and classifier steps on a sample dataset with as high a score (accuracy on the test set) as possible during a time period of five to ten minutes"},
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:content {:text "o avoid operators that were potentially useful in user tasks, the overview used both a non-sklearn operator that was not available in the study versions of the tool as well as sklearn’s DummyClassifier that generates predictions without considering input features"},
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