Papers with Automated Machine Learning
AutoML for NLP (2023.eacl-tutorials)
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| Challenge: | Automated Machine Learning (AutoML) is an emerging field that has potential to impact how we build models in NLP. |
| Approach: | This tutorial will summarize the main AutoML techniques and illustrate how to apply them to improve the NLP model-building process. |
| Outcome: | This tutorial summarizes the main AutoML techniques and illustrates how to apply them to improve the model-building process. |
AutoML Meets Hugging Face: Domain-Aware Pretrained Model Selection for Text Classification (2025.naacl-srw)
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| Challenge: | Existing methods for fine-tuning text classification models are resource-intensive and require substantial computational power and time. |
| Approach: | They propose a corpus-driven domain mapping pipeline that integrates pre-fine-tuned models from Hugging Face Model Hub into AutoML systems to improve model selection. |
| Outcome: | The proposed pipeline improves model selection and streamlines workflows and reduces computational costs. |
AUTOSUMM: Automatic Model Creation for Text Summarization (2021.emnlp-main)
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Sharmila Reddy Nangi, Atharv Tyagi, Jay Mundra, Sagnik Mukherjee, Raj Snehal, Niyati Chhaya, Aparna Garimella
| Challenge: | Recent efforts to develop deep learning models for text generation tasks are challenging for non-experts. |
| Approach: | They propose methods to automatically create deep learning models for extractive and abstractive summarization tasks using large language models. |
| Outcome: | The proposed methods achieve near state-of-the-art performance on a range of datasets. |