NCA-GENL Question 2
Select 2You are tasked with building a generative AI model for a company that wants to predict and autocomplete customer support queries based on historical data. Which of the following techniques or algorithms would be most appropriate to implement this solution?
- A
Reinforcement learning to optimize query responses based on user feedback
- B
Supervised learning using labeled historical query-response pairs
- C
Unsupervised learning to cluster similar customer queries
- D
Transfer learning using a pre-trained language model fine-tuned on domain-specific data
- E
K-Nearest Neighbors (KNN) for query classification
Show answer and explanation
Correct answers: B, D
Explanation
The best techniques for this scenario are supervised learning and transfer learning. Supervised learning is effective for training models on historical query-response pairs, while transfer learning leverages pre-trained language models, which can be fine-tuned to adapt to the specific domain, significantly improving their capability to autocomplete and predict queries. Other options, such as reinforcement learning or unsupervised learning, do not directly address the requirements of the task, and KNN lacks the sophistication required for generative AI tasks.
- A. Incorrect.
Reinforcement learning is not the best choice here because it focuses on sequential decision-making and often requires real-time feedback, which is not the primary goal in this scenario.
- B. Correct.
Supervised learning is appropriate as it uses labeled historical query-response pairs to train the model, making it effective for learning specific patterns in the customer support data.
- C. Incorrect.
Unsupervised learning is not sufficient for this task since clustering queries does not directly address the need to generate or predict specific query responses.
- D. Correct.
Transfer learning is a highly effective choice because pre-trained language models can be fine-tuned on domain-specific customer support data to improve performance on prediction and autocompletion tasks.
- E. Incorrect.
K-Nearest Neighbors (KNN) is a simple classification algorithm and is not well-suited for complex tasks like query prediction and generation.