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In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point.
Solution: We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.
Why? The abstract focusses on designing an adaptive loss scaling method, hence the generated output is correct.

New input: Recent pretrained transformer-based language models have set state-of-the-art performances on various NLP datasets. However, despite their great progress, they suffer from various structural and syntactic biases. In this work, we investigate the lexical overlap bias, e.g., the model classifies two sentences that have a high lexical overlap as entailing regardless of their underlying meaning. To improve the robustness, we enrich input sentences of the training data with their automatically detected predicate-argument structures. This enhanced representation allows the transformer-based models to learn different attention patterns by focusing on and recognizing the major semantically and syntactically important parts of the sentences. We evaluate our solution for the tasks of natural language inference and grounded commonsense inference using the BERT, RoBERTa, and XLNET models. We evaluate the models' understanding of syntactic variations, antonym relations, and named entities in the presence of lexical overlap. Our results show that the incorporation of predicate-argument structures during fine-tuning considerably improves the robustness, e.g., about 20pp on discriminating different named entities, while it incurs no additional cost at the test time and does not require changing the model or the training procedure.
Solution:
Enhancing the robustness of pretrained transformer models against the lexical overlap bias by extending the input sentences of the training data with their corresponding predicate-argument structures