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docs(website): highlight benchmarks

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Johann Schopplich
2025-11-18 10:14:07 +01:00
parent 9bebbb4070
commit 0ac629a085
9 changed files with 33 additions and 46 deletions
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benchmarks/src/report.ts
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@@ -275,9 +275,6 @@ ${modelPerformance}
</details>
<details>
<summary><strong>How the benchmark works</strong></summary>
#### What's Being Measured
This benchmark tests **LLM comprehension and data retrieval accuracy** across different input formats. Each LLM receives formatted data and must answer questions about it. This does **not** test the model's ability to generate TOON output – only to read and understand it.
@@ -296,7 +293,7 @@ Eleven datasets designed to test different structural patterns and validation ca
**Structural validation datasets:**
7. **Control**: Valid complete dataset (baseline for validation)
8. **Truncated**: Array with 3 rows removed from end (tests [N] length detection)
8. **Truncated**: Array with 3 rows removed from end (tests \`[N]\` length detection)
9. **Extra rows**: Array with 3 additional rows beyond declared length
10. **Width mismatch**: Inconsistent field count (missing salary in row 10)
11. **Missing fields**: Systematic field omissions (no email in multiple rows)
@@ -319,14 +316,14 @@ ${totalQuestions} questions are generated dynamically across five categories:
- Example: "How many employees in Sales have salary > 80000?" → \`5\`
- Example: "How many active employees have more than 10 years of experience?" → \`8\`
- **Structure awareness (${structureAwarenessPercent}%)**: Tests format-native structural affordances (TOON's [N] count and {fields}, CSV's header row)
- **Structure awareness (${structureAwarenessPercent}%)**: Tests format-native structural affordances (TOON's \`[N]\` count and \`{fields}\`, CSV's header row)
- Example: "How many employees are in the dataset?" → \`100\`
- Example: "List the field names for employees" → \`id, name, email, department, salary, yearsExperience, active\`
- Example: "What is the department of the last employee?" → \`Sales\`
- **Structural validation (${structuralValidationPercent}%)**: Tests ability to detect incomplete, truncated, or corrupted data using structural metadata
- Example: "Is this data complete and valid?" → \`YES\` (control dataset) or \`NO\` (corrupted datasets)
- Tests TOON's [N] length validation and {fields} consistency checking
- Tests TOON's \`[N]\` length validation and \`{fields}\` consistency checking
- Demonstrates CSV's lack of structural validation capabilities
#### Evaluation Process
@@ -341,8 +338,6 @@ ${totalQuestions} questions are generated dynamically across five categories:
- **Token counting**: Using \`gpt-tokenizer\` with \`o200k_base\` encoding (GPT-5 tokenizer)
- **Temperature**: Not set (models use their defaults)
- **Total evaluations**: ${totalQuestions} questions × ${formatCount} formats × ${modelNames.length} models = ${totalEvaluations.toLocaleString('en-US')} LLM calls
</details>
`.trim()
}
@@ -398,7 +393,10 @@ function generateSummaryComparison(
if (!toon || !json)
return ''
return `**Key tradeoff:** TOON achieves **${(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.`
return `
> [!TIP] Results Summary
> 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.
`.trim()
}
/**