Solving hallucination in LLMs is complex and may not be entirely possible. However, techniques such as reinforcement learning from human feedback (RLHF) have shown some success in reducing hallucinations. Despite the issues, some argue that hallucination could fuel creativity by producing unexpected outputs. The best approach currently seems to be treating models' predictions with skepticism.
Key takeaways:
- Large language models (LLMs) like OpenAI’s ChatGPT have a tendency to invent 'facts', a phenomenon known as hallucination, due to the way they are developed and trained.
- LLMs are statistical systems that predict data based on patterns and context from a large number of examples, usually sourced from the public web.
- While it's unlikely that hallucination can be completely eliminated, there are ways to reduce it, such as curating a high-quality knowledge base for the LLM to draw on, or using reinforcement learning from human feedback (RLHF).
- Despite the issues with hallucination, it can have creative applications and can lead to the novel connection of ideas. However, it's important to treat models' predictions with skepticism.