Fine-Tuning for Better Performance
Fine-tuning helps improve LLM performance by making them understand human-like prompts and generate natural-sounding language. However, models can sometimes generate harmful or incorrect responses, known as hallucinations. For example, a model may provide wrong health advice confidently.
Improving with Human Feedback
To make LLMs more accurate, fine-tuning with human feedback is crucial. This approach aligns models with human values, ensuring they generate helpful, honest, and harmless responses while reducing toxic language and incorrect information.
Model Interpretability
When selecting models, consider interpretability, which is how easy it is to understand the model’s decisions. There is a trade-off between model performance and interpretability.
- Simple models are easier to interpret but may not perform as well.
- Complex models (e.g., neural networks) perform better but are harder to understand.
There are two main methods for model interpretability:
- Intrinsic Analysis: Used for simple models with low complexity.
- Post Hoc Analysis: Applied to both simple and complex models, it interprets predictions made by the model.
Performance Metrics
To evaluate and compare models, developers use metrics like ROUGE and BLEU:
- ROUGE: Used to evaluate text summaries by comparing them to human-generated summaries.
- BLEU: Evaluates machine translation by comparing the output with human-generated translations.
These metrics help developers understand how well their models are performing.
