Hallucinations in LLMs Are Not a Bug in the Data

Towards Data Science / 3/17/2026

💬 OpinionIdeas & Deep Analysis

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Key Points

The article argues that hallucinations in LLMs are not solely due to data quality but are a feature of the transformer-based generation architecture itself.
It reframes evaluation and risk management, suggesting that reliability improvements require architectural understanding and system-level controls rather than only dataset cleansing.
The piece highlights practical implications for developers, such as employing retrieval-augmented generation, verification layers, and careful prompt design to mitigate errors.
It challenges the notion that hallucinations can be fixed purely by better data and urges redesigned tooling and metrics that account for the probabilistic nature of LLM outputs.

It’s a feature of the architecture