Pretrained language models (PLMs) are revolutionizing natural language processing by enabling machines to understand and generate human-like text. Pretrained language models are neural networks that have been trained on massive amounts of text data to learn the structure and patterns of human language. These models can then be fine-tuned for specific tasks, such as machine translation, sentiment analysis, or text classification. By leveraging the knowledge gained during pretraining, PLMs can achieve state-of-the-art performance on a wide range of natural language processing tasks. Recent research has explored various aspects of pretrained language models, such as extending them to new languages, understanding their learning process, and improving their efficiency. One study focused on adding new subwords to the tokenizer of a multilingual pretrained model, allowing it to be applied to previously unsupported languages. Another investigation delved into the 'embryology' of a pretrained language model, examining how it learns different linguistic features during pretraining. Researchers have also looked into the effect of pretraining on different types of data, such as social media text or domain-specific corpora. For instance, one study found that pretraining on downstream datasets can yield surprisingly good results, even outperforming models pretrained on much larger corpora. Another study proposed a back-translated task-adaptive pretraining method, which augments task-specific data using back-translation to improve both accuracy and robustness in text classification tasks. Practical applications of pretrained language models can be found in various industries. In healthcare, domain-specific models like MentalBERT have been developed to detect mental health issues from social media content, enabling early intervention and support. In the biomedical field, domain-specific pretraining has led to significant improvements in tasks such as named entity recognition and relation extraction, facilitating research and development. One company leveraging pretrained language models is OpenAI, which developed the GPT series of models. These models have been used for tasks such as text generation, translation, and summarization, demonstrating the power and versatility of pretrained language models in real-world applications. In conclusion, pretrained language models have become a cornerstone of natural language processing, enabling machines to understand and generate human-like text. By exploring various aspects of these models, researchers continue to push the boundaries of what is possible in natural language processing, leading to practical applications across numerous industries.
Pretraining and Fine-tuning
What is the difference between pretraining and fine-tuning?
Pretraining and fine-tuning are two essential techniques in machine learning that enable models to learn from large datasets and adapt to specific tasks. Pretraining involves training a model on a large dataset to learn general features and representations, capturing the underlying structure of the data. Fine-tuning, on the other hand, involves adapting the pretrained model to a specific task using a smaller, task-specific dataset. This process allows the model to refine its knowledge and improve its performance on the target task.
What does pretraining mean?
Pretraining is a technique in machine learning where a model is initially trained on a large dataset to learn general features and representations. This process helps the model capture the underlying structure of the data and develop a strong foundation for further learning. Pretraining is often used as a starting point for fine-tuning, where the model is then adapted to a specific task using a smaller, task-specific dataset.
What does pretraining a model mean?
Pretraining a model means training the model on a large dataset before adapting it to a specific task. This initial training helps the model learn general features and representations, capturing the underlying structure of the data. Pretraining provides a strong foundation for further learning, allowing the model to be fine-tuned on a smaller, task-specific dataset to improve its performance on the target task.
What is pre-training and fine-tuning in NLP?
In natural language processing (NLP), pretraining and fine-tuning are techniques used to train models on large text corpora and adapt them to specific tasks. Pretraining involves training a model on a large text corpus to learn general language features and representations. Fine-tuning, on the other hand, involves adapting the pretrained model to a specific NLP task, such as sentiment analysis or machine translation, using a smaller, task-specific dataset. This process allows the model to refine its knowledge and improve its performance on the target task.
How do pretraining and fine-tuning improve machine learning model performance?
Pretraining and fine-tuning improve machine learning model performance by leveraging the knowledge gained from large datasets and adapting it to specific tasks. Pretraining helps the model learn general features and representations from a large dataset, capturing the underlying structure of the data. Fine-tuning then refines the model's knowledge using a smaller, task-specific dataset, allowing it to perform better on the target task. This combination of techniques enables models to benefit from both the vast amounts of data available for pretraining and the specialized knowledge required for specific tasks.
What are some recent advancements in pretraining and fine-tuning techniques?
Recent advancements in pretraining and fine-tuning techniques include two-stage fine-tuning, which first fine-tunes the final layer of the pretrained model with class-balanced reweighting loss and then performs standard fine-tuning. This method has shown promising results in handling class-imbalanced data and improving performance on tail classes with few samples. Another notable development is the cross-modal fine-tuning framework, ORCA, which extends the applicability of a single large-scale pretrained model to diverse modalities. ORCA aligns the embedded feature distribution with the pretraining modality and then fine-tunes the pretrained model on the embedded data, achieving state-of-the-art results on various benchmarks.
What are some practical applications of pretraining and fine-tuning?
Practical applications of pretraining and fine-tuning include natural language processing, computer vision, and drug discovery. In NLP, pretrained language models have demonstrated outstanding performance in tasks requiring social and emotional commonsense reasoning. In computer vision, hierarchical pretraining has been shown to decrease convergence time, improve accuracy, and enhance the robustness of self-supervised pretraining. In drug discovery, researchers have investigated the impact of self-supervised pretraining on small molecular data and found that the benefits can be negligible in some cases. However, with additional supervised pretraining, improvements can be observed, especially when using richer features or more balanced data splits.
Pretraining and Fine-tuning Further Reading
1.Two-Stage Fine-Tuning: A Novel Strategy for Learning Class-Imbalanced Data http://arxiv.org/abs/2207.10858v1 Taha ValizadehAslani, Yiwen Shi, Jing Wang, Ping Ren, Yi Zhang, Meng Hu, Liang Zhao, Hualou Liang2.Cross-Modal Fine-Tuning: Align then Refine http://arxiv.org/abs/2302.05738v2 Junhong Shen, Liam Li, Lucio M. Dery, Corey Staten, Mikhail Khodak, Graham Neubig, Ameet Talwalkar3.Go Beyond Plain Fine-tuning: Improving Pretrained Models for Social Commonsense http://arxiv.org/abs/2105.05913v1 Ting-Yun Chang, Yang Liu, Karthik Gopalakrishnan, Behnam Hedayatnia, Pei Zhou, Dilek Hakkani-Tur4.DP-RAFT: A Differentially Private Recipe for Accelerated Fine-Tuning http://arxiv.org/abs/2212.04486v2 Ashwinee Panda, Xinyu Tang, Vikash Sehwag, Saeed Mahloujifar, Prateek Mittal5.Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes http://arxiv.org/abs/2211.13638v1 Yiqiao Jin, Xiting Wang, Yaru Hao, Yizhou Sun, Xing Xie6.Multi-pretrained Deep Neural Network http://arxiv.org/abs/1606.00540v1 Zhen Hu, Zhuyin Xue, Tong Cui, Shiqiang Zong, Chenglong He7.Extending the Subwording Model of Multilingual Pretrained Models for New Languages http://arxiv.org/abs/2211.15965v1 Kenji Imamura, Eiichiro Sumita8.Downstream Datasets Make Surprisingly Good Pretraining Corpora http://arxiv.org/abs/2209.14389v1 Kundan Krishna, Saurabh Garg, Jeffrey P. Bigham, Zachary C. Lipton9.Does GNN Pretraining Help Molecular Representation? http://arxiv.org/abs/2207.06010v2 Ruoxi Sun, Hanjun Dai, Adams Wei Yu10.Self-Supervised Pretraining Improves Self-Supervised Pretraining http://arxiv.org/abs/2103.12718v2 Colorado J. Reed, Xiangyu Yue, Ani Nrusimha, Sayna Ebrahimi, Vivek Vijaykumar, Richard Mao, Bo Li, Shanghang Zhang, Devin Guillory, Sean Metzger, Kurt Keutzer, Trevor DarrellExplore More Machine Learning Terms & Concepts
Pretrained Language Models Principal Component Analysis (PCA) Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction and feature extraction in machine learning, enabling efficient data processing and improved model performance. Principal Component Analysis (PCA) is a statistical method that simplifies complex datasets by reducing their dimensionality while preserving the most important information. It does this by transforming the original data into a new set of uncorrelated variables, called principal components, which are linear combinations of the original variables. The first principal component captures the largest amount of variance in the data, while each subsequent component captures the maximum remaining variance orthogonal to the previous components. Recent research has explored various extensions and generalizations of PCA to address specific challenges and improve its performance. For example, Gini PCA is a robust version of PCA that is less sensitive to outliers, as it relies on city-block distances rather than variance. Generalized PCA (GLM-PCA) is designed for non-normally distributed data and can incorporate covariates for better interpretability. Kernel PCA extends PCA to nonlinear cases, allowing for more complex spatial structures in high-dimensional data. Practical applications of PCA span numerous fields, including finance, genomics, and computer vision. In finance, PCA can help identify underlying factors driving market movements and reduce noise in financial data. In genomics, PCA can be used to analyze large datasets with noisy entries from exponential family distributions, enabling more efficient estimation of covariance structures and principal components. In computer vision, PCA and its variants, such as kernel PCA, can be applied to face recognition and active shape models, improving classification performance and model construction. One company case study involves the use of PCA in the semiconductor industry. Optimal PCA has been applied to denoise Scanning Transmission Electron Microscopy (STEM) XEDS spectrum images of complex semiconductor structures. By addressing issues in the PCA workflow and introducing a novel method for optimal truncation of principal components, researchers were able to significantly improve the quality of denoised data. In conclusion, PCA and its various extensions offer powerful tools for simplifying complex datasets and extracting meaningful features. By adapting PCA to specific challenges and data types, researchers continue to expand its applicability and effectiveness across a wide range of domains.
