Contextual compression in Prompt Engineering / GenAI - Model Metrics & Evaluation

Metrics & Evaluation - Contextual compression

Which metric matters for Contextual Compression and WHY

Contextual compression reduces data size while keeping important meaning. The key metric is Reconstruction Quality, often measured by Perplexity or BLEU score in language tasks. This shows how well the compressed data can be restored or understood. Another important metric is Compression Ratio, which tells how much smaller the data became. We want a good balance: high quality with strong compression.

Confusion Matrix or Equivalent Visualization

Contextual compression is not a classification task, so no confusion matrix applies. Instead, we use a quality vs compression table or graph. For example:

    +----------------+------------------+
    | Compression %  | Reconstruction   |
    | (smaller is    | Quality (e.g.,   |
    | better)        | BLEU score)      |
    +----------------+------------------+
    | 50%            | 0.85             |
    | 30%            | 0.75             |
    | 20%            | 0.60             |
    +----------------+------------------+
    

This shows how quality drops as compression increases.

Precision vs Recall Tradeoff (or Equivalent)

In contextual compression, the tradeoff is between Compression Ratio and Reconstruction Quality. Compressing more saves space but risks losing important details. Compressing less keeps more meaning but uses more space.

For example, compressing a chat history too much might lose key context, making replies less accurate. Compressing lightly keeps context but costs more storage.

What "Good" vs "Bad" Metric Values Look Like

Good: Compression ratio around 30-50% with reconstruction quality (BLEU or similar) above 0.8 means data is much smaller but still clear.

Bad: Compression ratio below 20% with quality below 0.6 means too much info lost, making the compressed data useless.

Common Metrics Pitfalls

• Ignoring quality: Focusing only on compression ratio can lead to unusable data.
• Overfitting compression: Compressing too well on training data but failing on new data.
• Data leakage: Using future context in compression can give unrealistic quality.
• Misleading metrics: Using accuracy or classification metrics instead of reconstruction quality.

Self Check

Your compression model reduces data size by 70% but the reconstruction quality BLEU score is 0.4. Is it good for production? Why or why not?

Answer: No, it is not good. Although the data is much smaller, the low BLEU score means the compressed data loses too much meaning. This will hurt any task relying on the compressed context.

Key Result

Contextual compression balances compression ratio and reconstruction quality to keep meaning while reducing size.