README.md

---
language:
- ar
license: cc-by-4.0
task_categories:
- text-generation
- question-answering
task_ids:
- conversational
tags:
- arabic
- function-calling
- tool-use
- benchmark
- nlp
- llm-evaluation
- MSA
pretty_name: ArabFuncBench
size_categories:
- 1K<n<10K
---

# ArabFuncBench: A Native Arabic Benchmark for Evaluating Function Calling in Large Language Models

## Dataset Description

**ArabFuncBench** is the first natively constructed Arabic benchmark for evaluating 
function calling (tool use) in large language models. All utterances, tool descriptions, 
and argument values are written in Modern Standard Arabic (MSA) — not translated from English.

- **Paper:** [ArabFuncBench: A Native Arabic Benchmark for Evaluating Function Calling in Large Language Models] (under review)
- **Authors:** Lamyaa Sadouk — Ecole Marocaine des Sciences de l'Ingénieur, Casablanca, Morocco | Taoufiq Gadi — Laboratoire IRM, Morocco
- **Dataset:** https://huggingface.co/datasets/lsadouk1111/ArabFuncBench
- **Code:** https://github.com/lsadouk/ArabFuncBench
- **License:** CC-BY 4.0

---

## Dataset Summary

ArabFuncBench comprises **1,000 examples** across five real-world Arabic service domains:

| Domain | Tools | Positive | Negative | Total |
|---|---|---|---|---|
| Education | 10 | 160 | 40 | 200 |
| E-commerce | 10 | 160 | 40 | 200 |
| Healthcare | 10 | 160 | 40 | 200 |
| Islamic Services | 10 | 160 | 40 | 200 |
| Government | 10 | 160 | 40 | 200 |
| **Total** | **50** | **800** | **200** | **1,000** |

---

## Motivation

Arabic-speaking organizations increasingly deploy AI-powered digital services — 
government portals, hospital systems, banking interfaces, e-commerce platforms. 
Function calling is critical for these systems: the LLM must translate Arabic user 
intent into structured, executable API calls with correctly extracted Arabic argument values.

Despite rapid growth in Arabic LLM development, no standardized benchmark existed 
for evaluating this capability. ArabFuncBench fills this gap.

---

## Dataset Structure

### Files

- `arab_func_bench_examples.json` — 1,000 evaluation examples
- `arab_func_bench_tools.json` — 50 tool definitions in OpenAI JSON schema format
- `all_metrics.json` — Evaluation results for all 7 models

### Example Format

```json
{
  "id": "islamic_services_calculate_prayer_times_001",
  "domain": "islamic_services",
  "utterance": "متى موعد صلاة الفجر في الرياض اليوم؟",
  "is_negative": false,
  "expected_function": "calculate_prayer_times",
  "expected_arguments": {
    "city": "الرياض",
    "date": "اليوم"
  },
  "available_tools": [
    "calculate_prayer_times",
    "get_hadith",
    "find_nearest_mosque"
  ]
}
```

### Tool Definition Format

```json
{
  "name": "calculate_prayer_times",
  "description": "يحسب أوقات الصلاة الخمس لمدينة معينة وتاريخ محدد",
  "parameters": {
    "type": "object",
    "properties": {
      "city": {
        "type": "string",
        "description": "اسم المدينة"
      },
      "date": {
        "type": "string",
        "description": "التاريخ المطلوب"
      }
    },
    "required": ["city", "date"]
  }
}
```

---

## Evaluation Metrics

Three metrics are defined for Arabic function calling evaluation:

**Tool Selection Accuracy (TSA):** Whether the model correctly identifies the 
function to call. For negative examples, TSA=1 if the model correctly returns null.

**Argument Extraction F1 (AEF1):** Character-level fuzzy matching (θ=0.65) between 
predicted and expected argument values, conditioned on correct tool selection.

**Language Compliance Rate (LCR):** Whether argument values are returned in Arabic 
rather than English. Numeric values (IDs, dates) are excluded from this check.

---

## Benchmark Results

| Model | Type | Size | TSA | AEF1 | LCR |
|---|---|---|---|---|---|
| Llama-3.3-70B | Multilingual | 70B | 0.997 | 0.910 | 0.949 |
| Qwen2.5-7B | Multilingual | 7B | 0.992 | 0.827 | 0.942 |
| GPT-4o-mini | Multilingual | Proprietary | 0.978 | 0.876 | 0.932 |
| Claude Haiku | Multilingual | Proprietary | 0.924 | 0.927 | 0.938 |
| ALLaM-7B | Arabic specialized | 7B | 0.923 | 0.830 | 0.914 |
| Fanar-9B | Arabic specialized | 9B | 0.871 | 0.861 | 0.881 |
| AceGPT-7B | Arabic specialized | 7B | 0.224 | 0.708 | 0.833 |

### Key Findings

- Instruction-tuned multilingual models consistently outperform Arabic-specialized models
- ALLaM-7B nearly matches Claude Haiku on TSA (0.923 vs 0.924) — instruction tuning quality matters more than Arabic specialization
- AceGPT-7B defaults to null on 97% of positive examples — Arabic fine-tuning without structured output training is insufficient
- Education is the hardest domain (avg TSA 0.929) due to semantic overlap between functionally adjacent tools
- Fanar-9B and ALLaM-7B show highest Type 4 error rates — Arabic-specialized models revert to English argument values under JSON output constraints

---

## Domains

**Education:** School scheduling, student grades, attendance, homework, transcripts

**E-commerce:** Product search, order tracking, payments, discounts, returns

**Healthcare:** Appointments, lab results, medications, prescriptions, health reminders

**Islamic Services:** Prayer times, Quran verses, Hijri calendar, Zakat calculation, Qibla direction

**Government:** Passport renewal, vehicle registration, scholarships, driving license, birth certificates

---

## Construction

- Tool definitions: 50 manually reviewed tools in OpenAI JSON schema format
- Example generation: Claude Haiku API with domain-specific Arabic prompts
- Quality control: 200 examples manually validated (20% of dataset)
- Systematic fixes: Arabic-Indic numeral normalization, year reference updates, Latin coupon code repositioning
- Negative examples: Fully regenerated after detecting 20% label error rate in initial generation

---

## Evaluation Protocol

Zero-shot evaluation — no fine-tuning, no task-specific adaptation. Each model 
receives the Arabic utterance and available tool definitions in JSON schema format 
and must return a structured JSON response.

---

## Citation

@misc{sadouk2026arabfuncbench,
  title={ArabFuncBench: A Native Arabic Benchmark for Evaluating Function Calling in Large Language Models},
  author={Sadouk, Lamyaa and Gadi, Taoufiq},
  year={2026},
  howpublished={ResearchGate preprint},
  note={Under review at ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP).}
}

---

## License

This dataset is released under CC-BY 4.0. You are free to use, share, and adapt 
it for any purpose, provided appropriate credit is given.

---

## Contact

Lamyaa Sadouk — Ecole Marocaine des Sciences de l'Ingénieur, Casablanca, Morocco