Like, it literally creates answers out of thin air then sells it as if it's correct. It doesn't even try to get it right. What sort of redundancy is there in analyzing if the answer is correct before spewing it out? I thought LLMs were supposed to discern what the best answer is given what was said to it based on its training, yet it'll give answers that don't exist based on any training. It's not like it learned the wrong answer from a Reddit post and just posted what Reddit said. It legit is making up wrong answers then citing correct answers. It just outright gets it wrong almost on purpose.
Anyone understand why LLMs fail so much?
I understand they run correlations but how does it determine a wrong answer is the most correlated to the correct response given the prompt instead of the actual correct answer...
Mitigation Strategies
- Retrieval-Augmented Generation (RAG): Connecting the LLM to a curated, trustworthy knowledge base (like internal company documents) ensures the model answers based on factual data rather than its internal parametric memory.
- Real-time Validation: Using tools to verify links, DOIs, and facts immediately after they are generated.
- Prompt Engineering: Explicitly instructing the model to say "I don't know" rather than hallucinating.
- Human-in-the-loop: Requiring human experts to check AI-generated content, especially for high-stakes decisions, research, or content creation.
While techniques like reinforcement learning from human feedback (RLHF) have reduced the overall rate of hallucinations, it remains a persistent, inherent risk of current transformer-based architectures.