1. Questions to Consider for Model Integration:
- How fast does the model need to generate completions?
- What is your compute budget?
- Are you willing to trade off model performance for improved speed or reduced storage?
2. Inference Challenges:
- Deployment challenges arise when deploying models on-premises, cloud, or to edge devices. These challenges include:
- Compute resources
- Storage
- Low-latency requirements
3. Optimization Techniques:
- Reduce model size: This reduces inference latency as smaller models load faster, but it may decrease model performance.
- Other techniques:
- Use concise prompts.
- Reduce the size and number of retrieved snippets.
- Adjust inference parameters and generation.
4. Evaluation Metrics for Generative AI:
- Generative AI models have non-deterministic outputs, making evaluation more challenging than traditional models.
- Common evaluation metrics:
- ROUGE: Measures automatic summarization and machine translation quality.
- BLEU: Used for evaluating machine translation tasks.
- Accuracy or RMSE: Standard metrics for deterministic models.
5. Task-Specific Benchmarks:
- GLUE: A collection of tasks to evaluate general language understanding across multiple tasks like sentiment analysis and question answering.
- SuperGLUE: An extension of GLUE that includes more advanced tasks like multi-sentence reasoning.
- MMLU: Evaluates problem-solving abilities across various fields, like history, mathematics, and law.
- BIG-bench: A benchmark for tasks beyond current model capabilities, including areas like physics, reasoning, and software development.
- HELM: Aims to improve model transparency and provides guidance on model performance for tasks such as summarization and sentiment analysis.
6. Manual Evaluation:
- Human workers can manually evaluate model responses by comparing output from SageMaker JumpStart models or other models.
- Amazon SageMaker Clarify: Provides tools to evaluate and compare LLMs and create model evaluation jobs.
7. Amazon Bedrock Evaluation Module:
- Amazon Bedrock offers an evaluation module that:
- Compares generated responses
- Calculates BERTscore to evaluate semantic similarity and detect issues like hallucinations.
