mirror of
https://github.com/voson-wang/toon.git
synced 2026-01-29 23:34:10 +08:00
155 lines
5.8 KiB
Markdown
155 lines
5.8 KiB
Markdown
### Retrieval Accuracy
|
||
|
||
Tested across **3 LLMs** with data retrieval tasks:
|
||
|
||
```
|
||
gpt-5-nano
|
||
toon ████████████████████ 99.4% (158/159)
|
||
yaml ███████████████████░ 95.0% (151/159)
|
||
csv ██████████████████░░ 92.5% (147/159)
|
||
json ██████████████████░░ 92.5% (147/159)
|
||
xml ██████████████████░░ 91.2% (145/159)
|
||
|
||
claude-haiku-4-5
|
||
toon ███████████████░░░░░ 75.5% (120/159)
|
||
xml ███████████████░░░░░ 75.5% (120/159)
|
||
csv ███████████████░░░░░ 75.5% (120/159)
|
||
json ███████████████░░░░░ 75.5% (120/159)
|
||
yaml ███████████████░░░░░ 74.2% (118/159)
|
||
|
||
gemini-2.5-flash
|
||
xml ██████████████████░░ 91.8% (146/159)
|
||
csv █████████████████░░░ 86.2% (137/159)
|
||
toon █████████████████░░░ 84.9% (135/159)
|
||
json ████████████████░░░░ 81.8% (130/159)
|
||
yaml ████████████████░░░░ 78.6% (125/159)
|
||
```
|
||
|
||
**Advantage:** TOON achieves **86.6% accuracy** (vs JSON's 83.2%) while using **46.3% fewer tokens**.
|
||
|
||
<details>
|
||
<summary><strong>Performance by dataset and model</strong></summary>
|
||
|
||
#### Performance by Dataset
|
||
|
||
##### Uniform employee records (TOON optimal format)
|
||
|
||
| Format | Accuracy | Tokens | Correct/Total |
|
||
| ------ | -------- | ------ | ------------- |
|
||
| `toon` | 87.4% | 2.483 | 152/174 |
|
||
| `csv` | 82.8% | 2.337 | 144/174 |
|
||
| `yaml` | 83.9% | 4.969 | 146/174 |
|
||
| `json` | 83.9% | 6.347 | 146/174 |
|
||
| `xml` | 88.5% | 7.314 | 154/174 |
|
||
|
||
##### E-commerce orders with nested structures
|
||
|
||
| Format | Accuracy | Tokens | Correct/Total |
|
||
| ------ | -------- | ------ | ------------- |
|
||
| `toon` | 90.9% | 5.967 | 120/132 |
|
||
| `csv` | 93.9% | 6.735 | 124/132 |
|
||
| `yaml` | 87.1% | 7.328 | 115/132 |
|
||
| `json` | 87.9% | 9.694 | 116/132 |
|
||
| `xml` | 93.2% | 10.992 | 123/132 |
|
||
|
||
##### Time-series analytics data
|
||
|
||
| Format | Accuracy | Tokens | Correct/Total |
|
||
| ------ | -------- | ------ | ------------- |
|
||
| `csv` | 89.7% | 1.393 | 78/87 |
|
||
| `toon` | 88.5% | 1.515 | 77/87 |
|
||
| `yaml` | 83.9% | 2.938 | 73/87 |
|
||
| `json` | 88.5% | 3.665 | 77/87 |
|
||
| `xml` | 85.1% | 4.376 | 74/87 |
|
||
|
||
##### Top 100 GitHub repositories
|
||
|
||
| Format | Accuracy | Tokens | Correct/Total |
|
||
| ------ | -------- | ------ | ------------- |
|
||
| `toon` | 76.2% | 8.745 | 64/84 |
|
||
| `csv` | 69.0% | 8.513 | 58/84 |
|
||
| `yaml` | 71.4% | 13.129 | 60/84 |
|
||
| `json` | 69.0% | 15.145 | 58/84 |
|
||
| `xml` | 71.4% | 17.095 | 60/84 |
|
||
|
||
#### Performance by Model
|
||
|
||
##### gpt-5-nano
|
||
|
||
| Format | Accuracy | Correct/Total |
|
||
| ------ | -------- | ------------- |
|
||
| `toon` | 99.4% | 158/159 |
|
||
| `yaml` | 95.0% | 151/159 |
|
||
| `csv` | 92.5% | 147/159 |
|
||
| `json` | 92.5% | 147/159 |
|
||
| `xml` | 91.2% | 145/159 |
|
||
|
||
##### claude-haiku-4-5
|
||
|
||
| Format | Accuracy | Correct/Total |
|
||
| ------ | -------- | ------------- |
|
||
| `toon` | 75.5% | 120/159 |
|
||
| `xml` | 75.5% | 120/159 |
|
||
| `csv` | 75.5% | 120/159 |
|
||
| `json` | 75.5% | 120/159 |
|
||
| `yaml` | 74.2% | 118/159 |
|
||
|
||
##### gemini-2.5-flash
|
||
|
||
| Format | Accuracy | Correct/Total |
|
||
| ------ | -------- | ------------- |
|
||
| `xml` | 91.8% | 146/159 |
|
||
| `csv` | 86.2% | 137/159 |
|
||
| `toon` | 84.9% | 135/159 |
|
||
| `json` | 81.8% | 130/159 |
|
||
| `yaml` | 78.6% | 125/159 |
|
||
|
||
</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 model's ability to generate TOON output).
|
||
|
||
#### Datasets Tested
|
||
|
||
Four datasets designed to test different structural patterns:
|
||
|
||
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.
|
||
3. **Analytics** (60 days of metrics): Time-series data with dates and numeric values.
|
||
4. **GitHub** (100 repositories): Real-world data from top GitHub repos by stars.
|
||
|
||
#### Question Types
|
||
|
||
~160 questions are generated dynamically across three categories:
|
||
|
||
- **Field retrieval (50%)**: Direct value lookups
|
||
- Example: "What is Alice's salary?" → `75000`
|
||
- Example: "What is the customer name for order ORD-0042?" → `John Doe`
|
||
|
||
- **Aggregation (25%)**: Counting and summation tasks
|
||
- Example: "How many employees work in Engineering?" → `17`
|
||
- Example: "What is the total revenue across all orders?" → `45123.50`
|
||
|
||
- **Filtering (25%)**: Conditional queries
|
||
- Example: "How many employees in Sales have salary > 80000?" → `5`
|
||
- Example: "How many orders have total > 400?" → `12`
|
||
|
||
#### Evaluation Process
|
||
|
||
1. **Format conversion:** Each dataset is converted to all 5 formats (TOON, JSON, YAML, CSV, XML).
|
||
2. **Query LLM**: Each model receives formatted data + question in a prompt and extracts the answer.
|
||
4. **Validate with LLM-as-judge**: `gpt-5-nano` validates if the answer is semantically correct (e.g., `50000` = `$50,000`, `Engineering` = `engineering`, `2025-01-01` = `January 1, 2025`).
|
||
|
||
#### Models & Configuration
|
||
|
||
- **Models tested**: `gpt-5-nano`, `claude-haiku-4-5`, `gemini-2.5-flash`
|
||
- **Token counting**: Using `gpt-tokenizer` with `o200k_base` encoding (GPT-5 tokenizer)
|
||
- **Temperature**: 0 (for non-reasoning models)
|
||
- **Total evaluations**: 159 questions × 5 formats × 3 models = 2,385 LLM calls
|
||
|
||
</details>
|