Researchers test six LLM reliability layers on drug corpus

Researchers tested six reliability layers wrapped around language models to evaluate whether stacking them increases extraction accuracy on a drug adverse event corpus. They found that the layers sorted answers by trustworthiness and withheld doubtful ones, but corrected almost none of the errors. The study demonstrates that language models primarily lose at reading comprehension, and deterministic checks or secondary reliability layers cannot recover missing mentions or fix fundamental reading failures. Open article →