ATLAS is a suite of software tools to detect and mitigate hallucinations in AI or Large Language Models (LLMs) .
AI hallucinations are incorrect or misleading results that AI models generate. These errors can be caused by a variety of factors, including insufficient training data, incorrect assumptions made by the model, or biases in the data used to train the model. AI hallucinations can be a problem for AI systems that are used to make important decisions, such as medical diagnoses or financial trading.
AI models are trained on data, and they learn to make predictions by finding patterns in the data. However, if the training data is incomplete or biased, the AI model may learn incorrect patterns. This can lead to the AI model making incorrect predictions, or hallucinating. For example, an AI model that is trained on a dataset of medical images may learn to identify cancer cells. However, if the dataset does not include any images of healthy tissue, the AI model may incorrectly predict that healthy tissue is cancerous. This is an example of an AI hallucination.
ATLAS utilizes search to find results to queries and compare them with the response the LLM provides to detect any form of hallucination.
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