Learn how to address bias in LLMs for the Data Professionals skill assessment exam. Discover cost-effective techniques like fine-tuning with dual objectives for accuracy and fairness.
Table of Contents
Question
You built a large language model (LLM) that sorts pictures of dogs from pictures of cookies. To make it unbiased, you implemented some debiasing techniques. When you show it off to your teammates, they point out that the model seems less confident identifying cookies that resemble dog faces. How will you cost-effectively address this issue?
A. Create dual LLMs, one using adversarial testing, and the other without adversarial testing.
B. Fine-tune your LLM with a dual objective: accuracy and fair responses.
C. Fine-tune your LLM with a diverse range of data-augmented datasets.
D. Create dual LLMs, one for accurate classifications, and the other for unbiased outputs.
Answer
B. Fine-tune your LLM with a dual objective: accuracy and fair responses.
Explanation
When your LLM struggles with confidence in distinguishing between ambiguous data points (e.g., cookies resembling dog faces), it indicates a need for better alignment between accuracy and fairness. Fine-tuning the model with a dual objective ensures that it not only performs well on its primary task (classification) but also minimizes bias in its decision-making process. Here’s why this approach is the most effective:
Dual Objective Fine-Tuning
This method involves training the model to optimize for both accuracy and fairness simultaneously. By explicitly incorporating fairness metrics into the loss function, the model learns to balance its performance across diverse scenarios, including edge cases like cookies resembling dog faces.
Fine-tuning allows you to adapt the model to specific tasks without retraining from scratch, making it cost-effective compared to building separate models or extensive retraining.
Advantages Over Other Options
Option A: Creating dual LLMs with and without adversarial testing is resource-intensive and does not directly address the issue of balancing accuracy and fairness.
Option C: While augmenting datasets can improve diversity, it may not guarantee fairness unless explicitly guided by fairness objectives during training.
Option D: Developing separate models for accuracy and unbiased outputs increases complexity and costs without ensuring cohesive performance.
Cost-Effectiveness
Fine-tuning leverages existing model architecture and parameters, reducing computational overhead compared to training new models or implementing complex adversarial techniques.
It also avoids the need for extensive new datasets, as fairness can be achieved by strategically re-weighting existing data during training.
By adopting this approach, you ensure that your LLM performs reliably across diverse inputs while maintaining ethical standards of fairness, a critical requirement in modern AI applications.
Large Language Models (LLMs) for Data Professionals skill assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Large Language Models (LLMs) for Data Professionals exam and earn Large Language Models (LLMs) for Data Professionals certification.