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Reducing unsafe responses in large language models — Level A2 — A large ruler mounted to the side of a wall

Reducing unsafe responses in large language modelsCEFR A2

26 Mar 2026

Level A2 – High beginner / Elementary
2 min
113 words

Large language models (LLMs) can give advice or instructions, so it is important that their responses are safe. A research team at a university studied how safety training works and tested new training ideas to reduce unsafe outputs while keeping good performance.

The researchers found two main problems. First, safety training can lower a model's accuracy, a problem called the alignment tax. Second, many models use a simple safety check that can be bypassed. The team proposed a hypothesis about this simple check and tested a method that freezes some model parts during fine-tuning to keep safety while the model learns new tasks. The work will be shown at an international conference.

Difficult words

  • modela computer program that generates or predicts text
    models, model's
  • safetybeing protected from dangerous or harmful outputs
    safety training, safety check
  • alignment taxloss of model accuracy after safety training
  • fine-tuningadditional training to adapt a model to new tasks
  • freezestop changing some parts during model training
    freezes
  • accuracyhow correct or precise a model's outputs are

Tip: hover, focus or tap highlighted words in the article to see quick definitions while you read or listen.

Discussion questions

  • Do you think it is okay if safety training lowers a model's accuracy? Why or why not?
  • Have you ever used advice from an AI or chatbot? Was it safe and helpful?
  • How would you check if a model's safety check can be bypassed?

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