Examtopics

コメント(6)

• 正解だと思う選択肢: A

  Explanation: By providing examples of text passages along with their corresponding sentiment labels (positive or negative), the model can learn from these examples how to classify the sentiment of the new text passage effectively

  👍 4

  jove2024/11/05
• 正解だと思う選択肢: A

  A: Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.

  Explanation: This strategy is known as few-shot prompting, where the prompt includes a few examples of labeled data (text passages with positive or negative sentiment) before asking the model to classify the new text passage. This helps the large language model (LLM) understand the task and align its output with the desired format.

  Why not the other options? B: Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt: Explaining the concept of sentiment analysis is unnecessary for the model, as it does not improve the model's ability to classify text. C: Provide the new text passage to be classified without any additional context or examples: Without examples, the LLM might not correctly infer the task or format of the output, leading to inconsistent or incorrect results.

  👍 4

  Moon2024/12/31
• 正解だと思う選択肢: A

  This approach uses few-shot learning, which is highly effective with large language models. By providing examples of text passages with their corresponding sentiment classifications, the LLM learns the context and pattern needed to classify the new passage.

  👍 1

  Gianiluca2024/12/23