One effective way to reduce hallucinations in LLMs is to implement an iterative feedback loop with domain experts. By having teachers review the generated test papers and provide corrections or adjustments, you can refine the model's outputs and reinforce accuracy.
We ran into this ourselves when developing an AI-assisted tool for educational content. Initially, we found that the model would sometimes produce irrelevant questions, so we brought in educators to help fine-tune the prompts and outputs, which significantly improved the results.
We ended up building Wyshbone to enhance our approach to content generation and ensure that the questions align closely with the provided materials, integrating feedback directly into our workflow.
We ran into this ourselves when developing an AI-assisted tool for educational content. Initially, we found that the model would sometimes produce irrelevant questions, so we brought in educators to help fine-tune the prompts and outputs, which significantly improved the results.
We ended up building Wyshbone to enhance our approach to content generation and ensure that the questions align closely with the provided materials, integrating feedback directly into our workflow.