To mitigate this, the article suggests using reinforcement learning with human feedback (RLHF). This method involves training the model to make decisions based on feedback from human evaluators who assess the quality of the generated text. Other potential solutions include domain-specific fine-tuning, adversarial training, and multi-modal models. Despite the challenges, the article highlights the immense opportunity to improve LLM outputs by including extra verification steps through RLHF.
Key takeaways:
- Large Language Models (LLMs) like ChatGPT can generate text that is coherent and contextually appropriate, but they are susceptible to "hallucination", where the model generates text that is factually incorrect or entirely fictional.
- LLM hallucination occurs due to a lack of ground truth from external sources, as the model's primary objective is to generate text that aligns with the patterns observed in the training data, which may contain inaccuracies, inconsistencies, and fictional content.
- Reinforcement learning with human feedback (RLHF) is a promising method to mitigate hallucinations in LLMs. It involves using human feedback as a reward signal to guide the model towards factual accuracy.
- Other active areas of research to mitigate hallucinations in LLMs include domain specific fine tuning, adversarial training, and multi-modal models. All these approaches require some level of verification for factual accuracy outside the model itself.