The LLM got the right answer for the wrong reason

The text explores the effectiveness of using an ontology alongside a schema when leveraging a language model to answer questions about business data. In experiments involving datasets such as FDA adverse event data and a proprietary SaaS churn dataset, the model's performance significantly improved when an ontology was included, scoring 10/10 with it compared to 3/10 with just the schema. The ontology provides a layer of business rules that clarifies the meaning of the data, which is crucial for accurate reasoning. The study highlights that schemas alone only inform the model of what data fields exist, not their significance or how to apply them, which can result in correct answers for the wrong reasons. This discrepancy can be perilous, as models might rely on training knowledge rather than specific dataset logic, especially in well-known domains. The ontology emerged from the AI Workbench's modeling workflow, capturing structured business rules crucial for aligning the model's reasoning with the data's intended interpretation.

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