Are AI models doomed to always hallucinate?

– LLMs, such as OpenAI's ChatGPT, exhibit a tendency called hallucination, where they generate inaccurate or fictional information.

Hallucination ranges from harmless inaccuracies, like false historical claims, to more harmful issues,  

– such as providing incorrect medical advice or causing legal troubles.

– These models are statistical systems trained on vast datasets to predict words and generate text based on patterns in the training data.

– The training process involves concealing previous words for context and having the model predict replacements, similar to predictive text systems.

– While this approach works well in generating coherent text, it cannot guarantee the accuracy of generated information.

– Hallucinations occur because LLMs lack the ability to estimate the uncertainty of their predictions and always produce an output, even when unsure.

– Researchers are exploring ways to mitigate hallucination, such as using high-quality knowledge bases for question answering systems.

– Reinforcement learning from human feedback (RLHF) has been employed to reduce hallucinations by fine-tuning models based on human-annotated rankings of generated text.

– There is a debate about whether hallucination can be completely eliminated or if it should be embraced for its potential to stimulate creativity.