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docs: clarify TOON's advantages and optimal data structure

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Johann Schopplich
2025-10-29 19:04:04 +01:00
parent 810748f862
commit 38ea864763
3 changed files with 12 additions and 10 deletions
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4
benchmarks/src/report.ts
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@@ -83,7 +83,7 @@ export function generateMarkdownReport(
// Build summary comparison
const summaryComparison = toon && json
? `**Advantage:** TOON achieves **${(toon.accuracy * 100).toFixed(1)}% accuracy** (vs JSON's ${(json.accuracy * 100).toFixed(1)}%) while using **${((1 - toon.totalTokens / json.totalTokens) * 100).toFixed(1)}% fewer tokens**.`
? `**Key tradeoff:** TOON achieves **${(toon.accuracy * 100).toFixed(1)}% accuracy** (vs JSON's ${(json.accuracy * 100).toFixed(1)}%) while using **${((1 - toon.totalTokens / json.totalTokens) * 100).toFixed(1)}% fewer tokens** on these datasets.`
: ''
// Build performance by dataset
@@ -221,7 +221,7 @@ This benchmark tests **LLM comprehension and data retrieval accuracy** across di
#### Datasets Tested
Four datasets designed to test different structural patterns:
Four datasets designed to test different structural patterns (all contain arrays of uniform objects, TOON's optimal format):
1. **Tabular** (${tabularSize} employee records): Uniform objects with identical fields – optimal for TOON's tabular format.
2. **Nested** (${nestedSize} e-commerce orders): Complex structures with nested customer objects and item arrays.