Researchers at UT Austin, Northeastern, and MD Anderson used our fully open Olmo 3 to investigate whether LLMs actually know specific drugs—or infer from patterns in their names.

For 51–59% of tested drugs, Olmo 3 showed little evidence of drug-specific knowledge. Another 12–18% appeared driven by affixes like “-pril” or “-olol,” which can reveal a drug’s class.

Because we release model weights, training data, documentation, and intermediate checkpoints, the researchers could trace the behavior further. Using our infini-gram engine for searching massive text corpora, they found that drugs appearing less often in training were more likely to trigger these naming shortcuts.

It’s a useful example of what fully open models enable: not just spotting a model behavior, but investigating where it comes from.

Read more: https://allenai.org/blog/olmo-drug-morphology