- Real-time monitoring: Track performance metrics (latency, accuracy) in production.
- Automated retraining: Continuously retrain the model based on feedback and evolving data.
- A/B testing: Compare the model’s performance over time to previous versions to ensure improvements are consistent.
- Error logging: Maintain detailed logs of failed cases and feedback for ongoing evaluation.
To manage resources:
- Cloud-based testing environments: Use scalable cloud solutions (e.g., AWS, Google Cloud) to run large-scale tests.
- Parallel processing: Distribute tests across multiple machines to optimize time and resources.
- Batch testing: Run tests in batches to reduce the demand on memory and processing power.
- Efficient resource allocation: Focus computational power on critical tests, and downscale less essential testing activities.
What metrics can be used to reliably assess the performance of LLMs, given the limitations of traditional metrics like perplexity and the diminishing trust in open-source benchmarks?
Anand Kannappan: Beyond traditional metrics like perplexity, which measures the uncertainty of a model’s predictions, consider metrics such as F1 score, precision, recall, and user satisfaction. Real-world performance benchmarks and user feedback can provide a more comprehensive assessment of LLM performance and its effectiveness in practical applications.