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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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benchmarks/results/retrieval-accuracy.md
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@@ -32,7 +32,7 @@ grok-4-fast-non-reasoning
csv █████████░░░░░░░░░░░ 45.5% (70/154)
```
**Advantage:** TOON achieves **69.2% accuracy** (vs JSON's 65.4%) while using **46.3% fewer tokens**.
**Key tradeoff:** TOON achieves **69.2% accuracy** (vs JSON's 65.4%) while using **46.3% fewer tokens** on these datasets.
<details>
<summary><strong>Performance by dataset and model</strong></summary>
@@ -132,7 +132,7 @@ 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** (100 employee records): Uniform objects with identical fields – optimal for TOON's tabular format.
2. **Nested** (50 e-commerce orders): Complex structures with nested customer objects and item arrays.