Defluffer promises -45% tokens. I measured the semantic cost of that savings and it's uncomfortable

1. 1Build a custom benchmark to measure semantic cost
2. 2Run experiments to compare token reduction and semantic loss
3. 3Analyze the results to determine the optimal balance between token count and context preservation
4. 4Apply the findings to improve the performance of Defluffer or similar tools
5. 5Evaluate the impact of token reduction on downstream NLP tasks

Developers and NLP engineers working with tokenization and text compression can benefit from understanding the trade-offs between token reduction and semantic cost.

💡 Token reduction can lead to significant loss of implicit context, affecting the performance of downstream NLP tasks.

🤖 Defluffer promises -45% tokens, but at what semantic cost? 📊