Large Language Models (LLM) have gained popularity, but they struggle with limitations such as hallucinations, lack of confidence estimates, and citations. Hallucinations occur when LLM generate inaccurate content that appears factual. Confidence estimates are crucial for determining factuality. OpenAI has made progress in addressing these issues. MIT researchers propose a method to create a more consistent LLM by curating high-quality training data and gradually expanding it. This approach could potentially lead to training diverse models with different worldviews. The idea of consistent data bootstrapping for LLM shows promise and deserves further exploration. (Word count: 100)
https://seanpedersen.github.io/posts/overcoming-llm-limits