Base Pretrained Language Model (LM)

A Base Pretrained Language Model (LM) is a Language Model that is pre-trained on a large corpus of text data without being fine-tuned for any specific downstream task.

Context:
- It can (typically) use unsupervised learning to understand and generate human-like text.
- It can (typically) be based on deep learning architectures like Transformers.
- It can (often) require substantial computational resources for training due to the size of the model and the dataset.
- It can (often) be applied in natural language processing tasks to leverage its broad understanding of language.
- It can (often) be the foundation for further training or fine-tuning on specific tasks, such as language translation, question-answering, or text summarization.
- It can range from (typically) being a Large Pretrained Base Language Model (Base LLM) to being a Small Pretrained Based Language Model.
- ...
Example(s):
- a Base Pretrained LLM, such as:
  - an OpenAI GPT Series Pretrained LLM, GPT-2, which is trained on a dataset of 8 million web pages.
  - BERT, which uses a masked language model approach for pretraining.
  - RoBERTa, an optimized version of BERT with improved pretraining techniques.
  - ...
Counter-Example(s):
- A Fine-Tuned Language Model, which is adapted specifically for a particular task like sentiment analysis.
- A Domain-Specific Language Model, which is trained primarily on text from a specific field, like medical journals.
See: Language Model, Transformer Architecture, Unsupervised Learning, Natural Language Processing.

References

(Radford et al., 2019) ⇒ Alec Radford, Jeffrey Wu, Rewon Child, et al. (2019). “Language Models are Unsupervised Multitask Learners.” In: OpenAI Blog.
- QUOTE: “Base pretrained language models like GPT-2 are designed to understand and generate human-like text, providing a broad linguistic base for further task-specific training.”

(Liu, Ott et al., 2019) ⇒ Yinhan Liu, Myle Ott, Naman Goyal, et al. (2019). “RoBERTa: A Robustly Optimized BERT Pretraining Approach.” In: arXiv.
- QUOTE: “RoBERTa, an example of a base pretrained language model, demonstrates improved performance on several benchmarks, showing the effectiveness of robust optimization in pretraining.”