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. |
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Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith
| Challenge: | Language models prerained on text from a wide variety of sources form the foundation of today’s NLP. |
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Cost-effective Selection of Pretraining Data: A Case Study of Pretraining BERT on Social Media (2020.findings-emnlp)
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| Challenge: | Recent studies show that domain-specific BERT models can be improved when in-domain data is used for pretraining. |
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Go Simple and Pre-Train on Domain-Specific Corpora: On the Role of Training Data for Text Classification (2020.coling-main)
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| Challenge: | Pre-trained language models provide the foundations for state-of-the-art performance across a wide range of natural language processing tasks, including text classification. |
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| Challenge: | Pretraining language models is expensive and data-intensive, but can it be improved? Several studies have found that directly pretraining on task data is more effective . |
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| Challenge: | Existing methods to improve text classification performance of pre-trained models have been used to improve their performance. |
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RecGPT: Generative Pre-training for Text-based Recommendation (2024.acl-short)
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| Challenge: | Existing models for text-based recommendation lack data sparsity and flexibility to capture fluctuations in user preferences over time. |
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Complicate Then Simplify: A Novel Way to Explore Pre-trained Models for Text Classification (2022.coling-1)
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| Challenge: | Existing frameworks for text classification employing pre-trained models are constrained by the difficulty of the task. |
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| Challenge: | Large-scale pre-trained language models are extensively trained on massive heterogeneous datasets, known as pre-training datasets. |
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| Challenge: | Contextualized word embeddings are becoming a ubiquitous component of natural language processing. |
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Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains (2021.findings-acl)
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| Challenge: | Large pre-trained models suffer from domain shift and are not optimal for specific domains. |
| Approach: | They propose a general approach to developing small, fast and effective pretrained models for specific domains by adapting off-the-shelf general pretrained model and performing task-agnostic knowledge distillation in target domains. |
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