---
license: cc-by-sa-4.0
pretty_name: "The Language–Energy Divide"
language:
  - en
  - zh
  - ru
  - fr
  - ps
  - ti
  - shn
  - bo
multilinguality:
  - multilingual
task_categories:
  - question-answering
  - text-generation
tags:
  - energy-efficiency
  - green-ai
  - multilingual
  - llm-inference
  - belebele
  - energy-measurement
size_categories:
  - 1K<n<10K
configs:
  - config_name: belebele_qwen3-8b_122lang
    data_files: results/belebele_canonical_Qwen3-8B_0shot.csv
---

<div align="center">

# 🌍⚡ The Language–Energy Divide

### Per-language energy measurements & prompts for multilingual LLM inference

[![Paper](https://img.shields.io/badge/📄_Paper-arXiv-b31b1b.svg)](https://arxiv.org/abs/XXXX.XXXXX)
[![Code](https://img.shields.io/badge/💻_Code-GitHub-181717.svg)](https://github.com/MichiganNLP/language-energy-divide)

</div>

This dataset accompanies the paper **"The Language–Energy Divide: Measuring Energy Costs of
Multilingual LLM Inference."** It releases the **per-language energy measurements** and the
**prompts** used in the study, so researchers can build on our numbers without re-running the
full sweep (≈ the entire measurement campaign).

> ⚡ **Per-token energy** varies by up to **8.3×** across languages · 🔋 **total energy** per
> request set varies by up to **179×** (English 17.6 kJ → Pashto 3,147 kJ) · 📉 the most
> energy-expensive languages are also the **least accurate**.

All energy is measured with the [ML.ENERGY Benchmark](https://arxiv.org/abs/2505.06371) (vLLM
serving + the [Zeus](https://ml.energy/zeus) library) and reported as **steady-state**
per-token energy. Hardware: **NVIDIA L40S** (main / cross-model / cross-task) and
**RTX 6000 Pro Blackwell** (batch-size sweep).

## 📦 Contents

### 📊 `results/` — energy measurements

| File | Description |
|------|-------------|
| `belebele_canonical_Qwen3-8B_0shot.csv` | ⭐ **Main result.** Qwen3-8B, Belebele, **122 languages**: energy/token, output tokens, total energy, accuracy (strict & lenient), resource level. |
| `belebele_canonical_Qwen3-14B.csv`, `…Qwen3-8B.csv`, `…Llama-3.1-8B.csv`, `…Llama-3.1-70B.csv` | Per-language energy for additional models (cross-model study). |
| `cross_model_12lang_table.csv` | Cross-model per-token energy comparison. |
| `crosstask_seqs256_summary.csv` | **Cross-task** (Belebele / GSM8K / LM-Arena) per-token energy, 8-language subset. |
| `belebele_seqsweep_l40s_0shot.csv` | **Batch-size sweep** on L40S (`max_num_seqs` ∈ {16,32,64,128,256,512}), 8 languages. |
| `seqsweep_belebele_v1_qwen8b_RTX6000_all8.csv` | Batch-size sweep on RTX 6000 Pro Blackwell, 8 languages. |

Key columns: `steady_energy_per_token_J` (J/token), `total_output_tokens`, `whole_gpu_energy_J`,
`accuracy_strict` / `accuracy_lenient`, `resource_level` (NLLB high/low).

### 📝 `prompts/` — the data used

| Path | Description |
|------|-------------|
| `belebele_instructions_all_languages.json` | Per-language **zero-shot CoT instructions + primers** for all 122 Belebele languages (NLLB-200 translated, back-translation QC, hand-curated where needed). |
| `belebele_instruction_manual.csv` | Human-readable table of the per-language instruction/primer translations + curation notes. |
| `gsm8k/gsm8k_<lang>_scored.jsonl` | **Machine-translated GSM8K** math prompts with back-translation and quality scores (`bertscore_f1`, `comet`). |
| `lmarena/<lang>_scored.jsonl` | **Machine-translated LM-Arena** open-chat prompts with back-translation and quality scores. |

Each translated prompt record: `id`, `prompt_en` (source), `prompt_tgt` (translation),
`back_en` (back-translation), `bertscore_f1`, `comet`. Translations passing the quality bar
(BERTScore ≥ 0.85, COMET ≥ 0.75) were retained.

## 🌐 The 8-language subset

Four high-resource — **English** (Latin), **Chinese** (Hans), **Russian** (Cyrillic),
**French** (Latin) — and four low-resource — **Southern Pashto** (Arabic), **Tigrinya**
(Ethiopic), **Shan** (Myanmar), **Tibetan** (Tibetan) — used for the cross-model, cross-GPU,
and cross-task experiments.

## 🧪 Quick start

```python
import pandas as pd
df = pd.read_csv("hf://datasets/MichiganNLP/language-energy-divide/results/belebele_canonical_Qwen3-8B_0shot.csv")
df.sort_values("steady_energy_per_token_J").head()
```

## 📚 Citation & attribution

```bibtex
@article{language-energy-divide,
  title  = {The Language--Energy Divide: Measuring Energy Costs of Multilingual LLM Inference},
  author = {Deng, Naihao and Shen, Alissa and Feng, Yiming and Nwatu, Joan and
            Chung, Jae-Won and Chowdhury, Mosharaf and Chen, Yulong and Mihalcea, Rada},
  year   = {2026}
}
```

Built on: **Belebele** ([Bandarkar et al., 2024](https://aclanthology.org/2024.acl-long.44/),
CC-BY-SA-4.0), **GSM8K** ([Cobbe et al., 2021](https://arxiv.org/abs/2110.14168), MIT),
**LM-Arena / Chatbot Arena** ([Chiang et al., 2024](https://arxiv.org/abs/2403.04132)),
translation via **NLLB-200** ([NLLB Team, 2022](https://arxiv.org/abs/2207.04672)), energy
measurement via the **ML.ENERGY Benchmark** ([Chung et al., 2025](https://arxiv.org/abs/2505.06371)).
Translated prompts derive from these sources; please also cite them and respect their licenses.

## ⚠️ Note on translations

GSM8K and LM-Arena prompts, and the Belebele per-language instructions/primers, are
machine-translated (with QC), not natively authored. They may not fully reflect real-world
usage of each language. Measured disparities are best read as a **lower bound** on what
speakers of underserved languages face with natively authored inputs.