Databricks Generative AI Engineer Associate Question 138
Select 3You are deploying a large language model (LLM) using Databricks, and your client requires safeguards to ensure the model does not generate harmful, biased, or inappropriate content. Which of the following strategies can you implement as guardrails to prevent negative outcomes?
- A
Implement content filtering by post-processing the model's responses using a blacklist of unsafe terms.
- B
Fine-tune the model using domain-specific data that emphasizes ethical and unbiased responses.
- C
Ignore harmful outputs during testing to focus on optimizing performance metrics like latency and accuracy.
- D
Leverage reinforcement learning from human feedback (RLHF) to align the model's behavior with ethical standards.
- E
Expose the model's outputs directly to end-users for testing without any moderation tools in place.
Show answer and explanation
Correct answers: A, B, D
Explanation
To implement effective guardrails for LLMs, it is essential to use strategies like content filtering, fine-tuning with ethical data, and reinforcement learning from human feedback (RLHF). These approaches help minimize the risk of harmful or biased outputs. Ignoring harmful outputs or exposing the model directly to users without safeguards can result in negative outcomes and should be avoided.
- A. Correct.
Implementing content filtering by post-processing is a standard guardrail strategy to prevent unsafe or harmful outputs. It helps ensure the model's responses adhere to predefined safety parameters.
- B. Correct.
Fine-tuning the model with domain-specific data that prioritizes ethical and unbiased responses helps reduce the likelihood of harmful or biased outputs directly at the model level.
- C. Incorrect.
Ignoring harmful outputs during testing is a bad practice as it can lead to deploying unsafe models. Testing should include rigorous evaluation of outputs to identify and mitigate risks.
- D. Correct.
Reinforcement learning from human feedback (RLHF) is a powerful method for aligning LLMs with ethical and safety standards, as it incorporates human judgment into the training process.
- E. Incorrect.
Exposing the model's outputs directly to end-users without moderation tools is a risky approach that can lead to harmful or inappropriate content being generated without safeguards.