Study finds six AI models act like two due to correlated errors
A new analysis published on Towards Data Science argues that ensembling multiple large language models yields fewer independent opinions than the number of models suggests. The author tested this hypothesis using six open-source models and 1,500 exam questions, finding that the models' mistakes are highly correlated. This means that when one model gets an answer wrong, the others are likely to…
Key points
- Six open models tested on 1,500 questions showed high error correlation.
- A formula calculates effective independent opinions, revealing six models act like two.
- Correlated mistakes reduce the reliability benefits of multi-model ensembling.
The article introduces a specific mathematical formula to calculate the effective number of independent opinions within a group of models. By applying this formula to the test data, the study demonstrates that six distinct models can function statistically like only two independent entities. This challenges the common assumption that simply adding more models to a voting or consensus mechanism will linearly improve accuracy or robustness.
The findings suggest that developers and researchers relying on multi-model ensembles for reliability may be overestimating the benefits of diversity. The core issue is not the quantity of models, but the similarity of their training data and architectures, which leads to systematic failures rather than random ones. This insight is crucial for anyone designing AI systems that depend on consensus mechanisms for critical tasks.
The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.
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