However, the article also highlights concerns about LLM providers potentially using user data to improve their models, as their terms and conditions often allow this. While some providers offer opt-out mechanisms, these are often opted-in by default and can be confusing. The article also mentions the introduction of "memory" features in some LLM tools, which allow them to remember small details for use in future conversations. Misunderstandings about how these models work can lead to misguided policy decisions and legislation.
Key takeaways:
- ChatGPT and similar tools do not directly learn from and memorize everything that users say to them. They function as stateless function calls, starting each conversation from scratch.
- There is a common misconception that 'training' these models involves them learning from each conversation, which is not the case. Training involves a lengthy and complex process of identifying patterns in large amounts of text data.
- While these models do not learn from individual conversations, many LLM providers do have terms and conditions that allow them to improve their models based on usage, which can lead to privacy concerns.
- There is a risk of policy decisions being made based on misconceptions about how these models work, potentially leading to measures that address invented rather than genuine risks.