LLM Prompt FORMATS make or break your LLM and RAG
AI Summary
- Introduction to the third video on in-context learning (ICL) with LLMs:
- Discusses extreme variance in LLM performance with in-context learning.
- In-context learning involves providing a text passage and specifying the desired answer format.
- Experiment with a Llama 27b model:
- Simple one-shot in-context learning example.
- Performance varies drastically with changes in prompt formatting:
- Separator changes can increase accuracy from 3% to over 50%.
- Further formatting tweaks can push performance to over 70%.
- Optimal formatting can lead to over 80% accuracy.
- Importance of prompt formatting:
- Semantically equivalent prompts can yield 3% to over 80% performance.
- Researchers define a grammar for prompt formats to test variations.
- Formatting choices, even invisible ones like spaces, significantly impact LLM performance.
- Case study on the impact of formatting:
- A study by various universities found that formatting choices massively influence prompt interpretation.
- Even small formatting changes can lead to large performance differences.
- Real-world implications:
- Standardized benchmarks may not reflect real-world applications.
- Prompt format optimization is crucial for improving LLM performance.
- Research findings:
- Performance spread is large regardless of model size or instruction tuning.
- Ignoring prompt format variance can negatively affect user experience.
- Personal takeaways:
- The need to develop prompt format optimization tools.
- Refine-tuning of LLMs with optimized prompt formats.
- Importance of maintaining coherent prompt formatting across all stages.
- Box and whisker plots:
- Explained for the audience to understand statistical data representation.
- Example provided to illustrate how to interpret a box plot.
The video emphasizes the critical role of prompt formatting in LLM performance and the potential for optimization to enhance model functionality.