A company's large language model (LLM) is experiencing hallucinations. How can the company decrease hallucinations?

Question

Options

• ASet up Agents for Amazon Bedrock to supervise the model training.
• BUse data pre-processing and remove any data that causes hallucinations.
• CDecrease the temperature inference parameter for the model.
• DUse a foundation model (FM) that is trained to not hallucinate.

• A
  3% (1)
• B
  13% (4)
• C
  77% (24)
• D
  6% (2)

Explanation

Hallucinations in large language models (LLMs) occur when the model generates outputs that are factually incorrect, irrelevant, or not grounded in the input data. To mitigate hallucinations, adjusting the model's inference parameters, particularly the temperature, is a well-documented approach in AWS AI Practitioner resources. The temperature parameter controls the randomness of the model's output. A lower temperature makes the model more deterministic, reducing the likelihood of generating creative but incorrect responses, which are often the cause of The temperature parameter controls the randomness of the generated text. Higher values (e.g., 0.8 or above) increase creativity but may lead to less coherent or factually incorrect outputs, while lower values (e.g., 0.2 or 0.3) make the output more focused and deterministic, reducing the likelihood of hallucinations.

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