Revisiting the Superficial Alignment Hypothesis Copy Copy

Your data is designed to be kept safe through several mechanisms:

1. Guardrails and Filters: The copilot includes features like blocking not-safe-for-work (NSFW) content and limiting responses to specific contexts. This ensures that sensitive or inappropriate content is not processed or generated, adding a layer of protection to your data.
2. User Access Control: You can control who has access to different data sets within the copilot by assigning roles and permissions. This restricts data access to authorized users only.
3. Bucket Assignment: Data is organized into specific buckets, and you can assign a copilot to only access data from a designated bucket. This ensures that your data is not mixed or improperly accessed by other parts of the system.
4. Third-Party Integration Controls: When connecting with third-party systems, you have control over which specific data sources are integrated, and how the copilot accesses them, helping to secure the flow of information.
5. Prompt Layer Security: Additional prompts can be used to control what data the copilot accesses and shares, further safeguarding your information.

Large language models, also known as LLMs, are very large deep learning models that are pre-trained on vast amounts of data. The underlying transformer is a set of neural networks that consist of an encoder and a decoder with self-attention capabilities. The encoder and decoder extract meanings from a sequence of text and understand the relationships between words and phrases in it.
Transformer LLMs are capable of unsupervised training, although a more precise explanation is that transformers perform self-learning. It is through this process that transformers learn to understand basic grammar, languages, and knowledge.
Unlike earlier recurrent neural networks (RNN) that sequentially process inputs, transformers process entire sequences in parallel. This allows the data scientists to use GPUs for training transformer-based LLMs, significantly reducing the training time.