NCA-GENL Question 147
Select 3You are part of a development team tasked with creating a custom fine-tuned Large Language Model (LLM) for a client. The team has implemented the software, but performance tests indicate that the model's responses are inconsistent under real-world user queries. Which actions should you take to address this issue?
- A
Implement prompt engineering to refine the model's input patterns.
- B
Retrain the model with a larger and more diverse dataset.
- C
Reduce the model's parameters to decrease computational complexity.
- D
Integrate a feedback loop that collects user inputs and corrections.
- E
Switch from fine-tuning to zero-shot learning for better results.
Show answer and explanation
Correct answers: A, B, D
Explanation
Improving the reliability of an LLM's responses typically involves refining the input structure through prompt engineering, retraining with better data to enhance generalization, and continuously learning from user feedback through a feedback loop. These approaches directly address the inconsistencies observed in real-world scenarios. Reducing model parameters and switching to zero-shot learning are less applicable or counterproductive to solving the described issue.
- A. Correct.
Prompt engineering can help improve the quality of model responses by providing better-structured inputs, making it easier for the model to generate consistent outputs.
- B. Correct.
Retraining the model with a larger and more diverse dataset can improve its generalization, addressing inconsistencies in its responses.
- C. Incorrect.
Reducing the model's parameters may decrease computational complexity, but it could also negatively impact the model's capability, making this an unsuitable solution for improving response consistency.
- D. Correct.
Integrating a feedback loop allows the system to learn from real-world user data and corrections, which helps the model adapt and improve over time.
- E. Incorrect.
While zero-shot learning can be effective in some scenarios, it is not a substitute for fine-tuning in cases where a customized model is required, especially when addressing issues of inconsistency.