| --- |
| pretty_name: Multi-SpecBench |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| - de |
| - es |
| - fr |
| - ja |
| - vi |
| - zh |
| multilinguality: multilingual |
| task_categories: |
| - text-generation |
| - translation |
| - summarization |
| - question-answering |
| tags: |
| - speculative-decoding |
| - llm-inference |
| - efficient-inference |
| - benchmark |
| - multilingual |
| - mt-bench |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: en |
| data_files: |
| - split: test |
| path: question_en.jsonl |
| - config_name: de |
| data_files: |
| - split: test |
| path: question_de.jsonl |
| - config_name: es |
| data_files: |
| - split: test |
| path: question_es.jsonl |
| - config_name: fr |
| data_files: |
| - split: test |
| path: question_fr.jsonl |
| - config_name: ja |
| data_files: |
| - split: test |
| path: question_ja.jsonl |
| - config_name: vi |
| data_files: |
| - split: test |
| path: question_vi.jsonl |
| - config_name: zh |
| data_files: |
| - split: test |
| path: question_zh.jsonl |
| - config_name: mix |
| data_files: |
| - split: test |
| path: question_mix.jsonl |
| --- |
| |
| # Multi-SpecBench |
|
|
| **Multi-SpecBench** is a multilingual extension of [Spec-Bench](https://github.com/hemingkx/Spec-Bench), spanning **7 languages × 7 task types**, for evaluating speculative decoding and other LLM inference-acceleration methods beyond English. It was introduced in [**AdaSpec: Adaptive Multilingual Speculative Decoding with Self-Synthesized Language-Aware Training and Vocabulary Simplification**](https://doi.org/10.1609/aaai.v40i36.40307) (Do, Le, and Nguyen; AAAI-26), a framework that combines language-aware drafter training with adaptive vocabulary simplification for speculative decoding. |
|
|
| Most existing speculative decoding benchmarks (including the original Spec-Bench) are English-only, which hides how well draft models and acceptance rates generalize to other languages and scripts. Multi-SpecBench extends the same task taxonomy to six additional languages and adds a code-mixed multilingual split, so inference-acceleration methods can be measured on non-English and cross-lingual workloads. |
|
|
| ## Dataset Summary |
|
|
| - **7 languages**: English (`en`), German (`de`), Spanish (`es`), French (`fr`), Japanese (`ja`), Vietnamese (`vi`), Chinese (`zh`) — each with an identical 560-prompt set — plus one additional **`mix`** split (588 prompts) sampling across all 7 languages for cross-lingual evaluation. |
| - **7 task types** per language, following the original Spec-Bench taxonomy |
| - **4,508 prompts total** across all 8 files. |
|
|
| ## Supported Uses |
|
|
| Multi-SpecBench is intended for benchmarking **inference-time acceleration methods** (vanilla autoregressive decoding, speculative decoding, EAGLE, FR-Spec, AdaSpec, etc.) on a target LLM across languages. Typical usage feeds each prompt to a decoding method under test and measures throughput / acceptance rate / speedup relative to autoregressive decoding, optionally cross-checking output quality against the provided `reference` fields where available. It is an **evaluation-only** benchmark — it is not intended for training. |
|
|
| ## Languages |
|
|
| | Config | Language | |
| |---|---| |
| | `en` | English | |
| | `de` | German | |
| | `es` | Spanish | |
| | `fr` | French | |
| | `ja` | Japanese | |
| | `vi` | Vietnamese | |
| | `zh` | Chinese | |
| | `mix` | Code-mixed sample drawn from all 7 languages above | |
|
|
| ## Dataset Structure |
|
|
| ### Loading |
|
|
| Each language (plus `mix`) is exposed as a separate config, each with a single `test` split: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("nguyenlab/Multi-SpecBench", "en", split="test") |
| print(ds[0]) |
| ``` |
|
|
| Available configs: `en`, `de`, `es`, `fr`, `ja`, `vi`, `zh`, `mix`. |
|
|
| ### Data Fields |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `question_id` | `int` | Index of the example in its original source dataset. IDs restart within each category and are **not unique across the whole file** — treat `(category, question_id)` as the unique key. | |
| | `category` | `string` | One of `summarization`, `qa`, `rag`, `translation`, `math_reasoning`, `code_generation`, or one of the 8 MT-Bench-style categories: `writing`, `roleplay`, `reasoning`, `math`, `coding`, `extraction`, `stem`, `humanities`. | |
| | `turns` | `list[string]` | The prompt. Length 1 for the 6 single-turn tasks, length 2 for the 8 MT-Bench-style categories (the second turn is a follow-up instruction). | |
| | `reference` | `list[string]`, optional | Reference answer(s), where available. Present for `summarization`, `rag`, `translation`, `math_reasoning`, and the MT-Bench categories `math`, `reasoning`, `coding`, `extraction`. Not present for `qa`, `code_generation`, `writing`, `roleplay`, `stem`, `humanities`. When present alongside a 2-turn prompt, `reference` has one entry per turn (an empty string if a turn has no judged reference). | |
| | `lang` | `string` | ISO 639-1 language code, matching the config/file. | |
|
|
| ## Licensing and Attribution |
|
|
| This dataset is released under **CC BY-NC 4.0**. It is derived from multiple upstream sources (CNN/DailyMail, Natural Questions, DPR, WMT14, GSM8K, HumanEval, MT-Bench) that carry their own licenses and terms of use; please review those upstream licenses before commercial use. |
|
|
| ## Citation |
|
|
| If you use Multi-SpecBench, please cite both the AdaSpec paper that introduces it and the original Spec-Bench benchmark it builds on: |
|
|
| ```bibtex |
| @article{do2026adaspec, |
| title = {AdaSpec: Adaptive Multilingual Speculative Decoding with Self-Synthesized Language-Aware Training and Vocabulary Simplification}, |
| author = {Do, Dinh-Truong and Le, Nguyen-Khang and Nguyen, Le-Minh}, |
| journal = {Proceedings of the AAAI Conference on Artificial Intelligence}, |
| volume = {40}, |
| number = {36}, |
| pages = {30530--30538}, |
| year = {2026}, |
| doi = {10.1609/aaai.v40i36.40307} |
| } |
| |
| @inproceedings{xia-etal-2024-unlocking, |
| title = {Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding}, |
| author = {Xia, Heming and Yang, Zhe and Dong, Qingxiu and Wang, Peiyi and Li, Yongqi and Ge, Tao and Liu, Tianyu and Li, Wenjie and Sui, Zhifang}, |
| booktitle = {Findings of the Association for Computational Linguistics: ACL 2024}, |
| month = aug, |
| year = {2024}, |
| pages = {7655--7671} |
| } |
| ``` |
|
|
| ## Acknowledgments |
|
|
| Multi-SpecBench builds directly on [Spec-Bench](https://github.com/hemingkx/Spec-Bench) and its underlying task datasets (CNN/DailyMail, Natural Questions, DPR, WMT14, GSM8K, HumanEval, MT-Bench). |
|
|
