In recent years, transformer-based language models have gained widespread attention in the field of natural language processing (NLP). These models have achieved state-of-the-art results on various tasks such as language translation and language generation. However, the inner workings of these models remain unclear to many researchers and practitioners. This survey aims to demystify transformer-based language models by providing an overview of their key components, architectures, and applications.
Tensor Train Format
One of the most important components of transformer-based language models is the tensor train (TT) format. The TT format is a way of representing high-dimensional data in a compact and efficient manner. It consists of a sequence of smaller tensors that are stacked together to form a larger tensor. Each of these smaller tensors represents a different factor in the TT decomposition. By optimizing the TT decomposition, researchers can reduce the computational complexity of their models while maintaining their accuracy.
Advantages and Challenges
One of the main advantages of transformer-based language models is their ability to handle long-range dependencies. Unlike traditional recurrent neural network (RNN) architectures, which only consider the immediate context of a word, transformers can capture context from distant parts of the input sequence. This allows them to generate more coherent and natural text. However, the computational complexity of transformer models can be quite high, making them challenging to train and deploy in some applications.
Applications
Transformer-based language models have a wide range of applications in NLP, including language translation, language generation, question answering, and text summarization. In these tasks, they often outperform traditional RNNs and other neural network architectures. However, the field is rapidly evolving, and new techniques are continually being developed to improve the performance and efficiency of transformer models.
Conclusion
In conclusion, transformer-based language models have revolutionized the field of NLP in recent years. Their ability to handle long-range dependencies has made them particularly useful for tasks such as language translation and generation. While there are still many challenges to overcome, the future of transformers looks bright. As researchers continue to develop new techniques and architectures, we can expect to see even more impressive results in the years to come.
