Incorporating multi-criteria feedback into models involves collecting and balancing diverse performance metrics or user preferences during training and refinement. For example, a model might need to optimize for accuracy, latency, user satisfaction, and fairness simultaneously. To achieve this, developers typically define a weighted loss function that combines these criteria, assign priorities to each based on domain requirements, and iteratively adjust the model’s behavior using techniques like gradient descent or reinforcement learning. This approach ensures the model doesn’t over-optimize for one metric at the expense of others.
A practical implementation might involve gathering explicit user ratings (e.g., thumbs-up/down), implicit signals like engagement duration, and system metrics like inference speed. For instance, a chatbot could prioritize reducing response time but also penalize answers that users flag as unhelpful. Developers might use techniques like multi-task learning, where separate model heads predict different criteria, or employ constrained optimization to enforce hard limits (e.g., “latency must not exceed 200ms”). Tools like Pareto optimization help identify trade-offs between conflicting goals, such as balancing model size (for efficiency) against prediction accuracy.
Testing and validation are critical. A/B testing can compare model versions using combined scoring (e.g., 60% accuracy, 30% speed, 10% fairness). For dynamic adjustment, online learning setups might update model weights in real time based on shifting user feedback. For example, a recommendation system could adapt weights for diversity versus relevance if users start skipping homogeneous suggestions. Frameworks like TensorFlow Extended (TFX) or custom pipelines often handle this by logging multi-dimensional feedback and retraining models periodically. The key is maintaining transparency in how criteria are weighted and ensuring the evaluation process mirrors real-world priorities.