Hallucinations are the Achilles heel of large language models. They can “hallucinate,” generating irrelevant, incorrect and even fabricated responses that can undermine end-user trust and satisfaction. Organizations that deploy LLM models need to take steps to prevent hallucinations from occurring.
Why Do Hallucinations Happen?
Hallucinations can result from factors related to a model’s training data, architecture and deployment. Two major sources of hallucinations are training data and inherent model structure.
Training data
If the training data is biased, the output will recreate those biases. If the data has errors in it, the output will recreate those errors. When the model is asked something that goes outside the scope of the training data, it often creates new data information in order to provide a response.
Inherent model structure
LLMs learn patterns from data and generate responses based on statistical likelihood rather than factual accuracy; the model doesn’t “know” anything. Rather, it gives the most likely response based on its structure. When there are gaps in the training data, LLM models are very good at filling in details that look and sound like they fit.
What Can You Do to Prevent Hallucinations?
The types of errors created by LLM models can create significant risks of liability, lost opportunity and reputation damage for the companies that deploy them, not to mention potential harm for their users. To prevent hallucinations, organizations that deploy LLMs should take the following steps.
Fine-tune the model
Fine-tuning on specific domains (rather than trying to capture the entirety of the world’s knowledge) increases the accuracy of answers. This means defining the scope of the model during design, selecting parameters most likely to bias the model toward accuracy and regularly evaluating results to intervene if it starts to go off-track.
Manage your model data
Training data for LLMs should be relevant and accurate. During training, it’s critical to ensure that the data is clean, well formatted and free of bias and errors.
Check results regularly
Use techniques like retrieval-augmented generation (RAG) to cross-reference outputs with verified data. In short – check the model’s work on a regular basis.
Train the model to bias toward accuracy instead of plausibility
This increases the chance that your LLM will return no answer (“I don’t know the answer to that question”) rather than making up a plausible-sounding answer that is not grounded in fact.
Train your users
End users will get better, more accurate results from LLMs with precise prompts. Training end users on how to construct effective queries for your LLM will limit poor responses to general questions.
Monitoring AI models
Continuous monitoring is critical to the successful deployment and use of LLMs. Part of deployment should be the creation of a monitoring framework – the process by which the model will be monitored for day-to-day operations, as well as the ongoing regular maintenance and upkeep of the model and data. Use of an AI observability solution specifically designed to monitor LLMs and LLM data can help organizations achieve success with their LLM deployments.