A company’s LLM produces hallucinated (incorrect) outputs. Which action can reduce hallucinations during inference?

Decreasing the temperature inference parameter makes the model's output more deterministic and less creative, thereby reducing the likelihood of generating hallucinated or factually incorrect information. Higher temperatures encourage more diverse and sometimes imaginative responses, which can lead to hallucinations. Setting up Agents for Amazon Bedrock supervises model training, not inference, and doesn't directly address hallucinations in an already trained model. Data pre-processing to remove data causing hallucinations is a good practice for training a model, but it doesn't directly solve the issue during inference with an existing model. While some FMs may be better at avoiding hallucinations, there's no FM specifically "trained to not hallucinate" as a universal solution, and this option doesn't describe an action to take with an existing model.