| --- |
| configs: |
| - config_name: llmrouter_generic |
| data_files: |
| - split: train |
| path: llmrouter_generic/train.parquet |
| - split: test |
| path: llmrouter_generic/test.parquet |
| - config_name: memory_locomo |
| data_files: |
| - split: train |
| path: memory_locomo/train.parquet |
| - split: test |
| path: memory_locomo/test.parquet |
| - config_name: memory_longmemeval |
| data_files: |
| - split: train |
| path: memory_longmemeval/train.parquet |
| - split: test |
| path: memory_longmemeval/test.parquet |
| - config_name: timeseries |
| data_files: |
| - split: train |
| path: timeseries/train.parquet |
| - split: test |
| path: timeseries/test.parquet |
| - config_name: video |
| data_files: |
| - split: train |
| path: video/train.parquet |
| - split: test |
| path: video/test.parquet |
| - config_name: multimodal_geometry3k |
| data_files: |
| - split: train |
| path: multimodal_geometry3k/train.parquet |
| - split: test |
| path: multimodal_geometry3k/test.parquet |
| - config_name: multimodal_mathvista |
| data_files: |
| - split: train |
| path: multimodal_mathvista/train.parquet |
| - split: test |
| path: multimodal_mathvista/test.parquet |
| - config_name: personalized |
| data_files: |
| - split: train |
| path: personalized/train.parquet |
| - split: test |
| path: personalized/test.parquet |
| - config_name: llm_candidates |
| data_files: |
| - split: train |
| path: llm_candidates/train.parquet |
| - config_name: llmrouter_generic_queries |
| data_files: |
| - split: train |
| path: llmrouter_generic_queries/train.parquet |
| - split: valid |
| path: llmrouter_generic_queries/valid.parquet |
| - split: test |
| path: llmrouter_generic_queries/test.parquet |
| - config_name: memory_locomo_queries |
| data_files: |
| - split: train |
| path: memory_locomo_queries/train.parquet |
| - split: valid |
| path: memory_locomo_queries/valid.parquet |
| - split: test |
| path: memory_locomo_queries/test.parquet |
| - config_name: memory_longmemeval_queries |
| data_files: |
| - split: train |
| path: memory_longmemeval_queries/train.parquet |
| - split: valid |
| path: memory_longmemeval_queries/valid.parquet |
| - split: test |
| path: memory_longmemeval_queries/test.parquet |
| - config_name: timeseries_queries |
| data_files: |
| - split: train |
| path: timeseries_queries/train.parquet |
| - split: valid |
| path: timeseries_queries/valid.parquet |
| - split: test |
| path: timeseries_queries/test.parquet |
| - config_name: video_queries |
| data_files: |
| - split: train |
| path: video_queries/train.parquet |
| - split: valid |
| path: video_queries/valid.parquet |
| - split: test |
| path: video_queries/test.parquet |
| - config_name: multimodal_geometry3k_queries |
| data_files: |
| - split: train |
| path: multimodal_geometry3k_queries/train.parquet |
| - split: valid |
| path: multimodal_geometry3k_queries/valid.parquet |
| - split: test |
| path: multimodal_geometry3k_queries/test.parquet |
| - config_name: multimodal_mathvista_queries |
| data_files: |
| - split: train |
| path: multimodal_mathvista_queries/train.parquet |
| - split: valid |
| path: multimodal_mathvista_queries/valid.parquet |
| - split: test |
| path: multimodal_mathvista_queries/test.parquet |
| - config_name: personalized_queries |
| data_files: |
| - split: train |
| path: personalized_queries/train.parquet |
| - split: valid |
| path: personalized_queries/valid.parquet |
| - split: test |
| path: personalized_queries/test.parquet |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - llm-routing |
| - model-selection |
| - benchmark |
| pretty_name: xRouteBench |
| --- |
| |
| # xRouteBench — LLM Routing Benchmark |
|
|
| **xRouteBench** is a benchmark for training and evaluating **LLM routers** — systems that pick the best LLM from a candidate pool for each incoming query, trading off **performance vs. price cost**. |
|
|
| Every query in each scenario was executed against **all 18 candidate LLMs**, recording each model's response, task performance, token usage, and latency. A router learns from the `train` split which model to pick, and is evaluated on `test`. |
|
|
| ## Scenarios (configs) |
|
|
| | Config | Domain | Train rows / queries | Test rows / queries | Metric | |
| |---|---|---|---|---| |
| | `llmrouter_generic` | 13 classic NLP benchmarks (MMLU, GSM8K, MATH, MBPP, ARC, …) | 80,802 / 4,487 | 67,122 / 3,729 | em_mc, GSM8K, MATH, code_eval, f1 | |
| | `memory_locomo` | Long-conversation memory QA (RAG top-k=5) | 15,930 / 885 | 5,652 / 314 | f1 | |
| | `memory_longmemeval` | Long-term memory eval (RAG top-k=5) | 4,986 / 277 | 1,818 / 101 | f1 | |
| | `timeseries` | Time-series understanding (7 sub-tasks) | 17,568 / 976 | 2,286 / 127 | mc | |
| | `video` | Egocentric video QA (Charades-Ego) | 3,618 / 201 | 486 / 27 | em | |
| | `multimodal_geometry3k` | Geometry math (multimodal) | 8,640 / 480 | 1,098 / 61 | em | |
| | `multimodal_mathvista` | Visual math reasoning | 14,400 / 800 | 1,800 / 100 | em, em_mc | |
| | `personalized` | Personalized preference (LLM-judge; chat-format queries) | 2,464 / 2,235 | 308 / 303 | llm_judge | |
| | `llm_candidates` | The 18-model candidate pool with pricing | 18 models | — | — | |
|
|
| ## Schema (routing data) |
|
|
| Each row = one (query, candidate model) pair: |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `task_name` | str | Sub-task the query belongs to (e.g. `gsm8k`, `mbpp`) | |
| | `query` | str | Full input prompt (personalized: JSON-encoded chat messages) | |
| | `ground_truth` | list/str | Reference answer(s) | |
| | `metric` | str | Scoring metric for this row | |
| | `choices` | str | Options for multiple-choice items (JSON-encoded) | |
| | `task_id` | str | ID within the sub-task | |
| | `model_name` | str | Candidate LLM this row was executed with | |
| | `response` | str | The model's actual response | |
| | `token_num` | int | Total tokens | |
| | `input_tokens` / `output_tokens` | int | Token breakdown (for price computation) | |
| | `response_time` | float | Latency in seconds | |
| | `performance` | float | Task score of this model on this query (0–1) | |
| | `embedding_id` | int | Index into precomputed query-embedding files (not included here) | |
|
|
|
|
| ## Raw query configs (`*_queries`) |
| |
| Each scenario also ships its **raw query set** (no model executions) as a |
| `<scenario>_queries` config with **train / valid / test** splits — use these to |
| run your own candidate models from scratch. Fields: `task_name`, `query`, |
| `ground_truth`, `metric`, `choices`, `task_id` (+ `conversation_id`/`category` |
| for the memory scenarios). The memory queries are the RAG top-k=5 turn-pair |
| variant used in the published experiments. Note: the `valid` split exists only |
| here; the routing-data configs have train/test. |
| |
| ## Candidate pool & pricing (`llm_candidates` config) |
| |
| 18 models spanning **$0.05–$1.25 per 1M input tokens** (25× spread) served via Together AI / NVIDIA NIM. |
| Row cost = `input_tokens × input_price/1e6 + output_tokens × output_price/1e6`. |
| |
| ## Usage |
| |
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("ulab-ai/xRouteBench", "llmrouter_generic") # any config name above |
| train, test = ds["train"], ds["test"] |
| |
| pricing = load_dataset("ulab-ai/xRouteBench", "llm_candidates")["train"] |
| ``` |
| |
| Composite reward for cost-aware routing (GraphRouter-style): |
| |
| ``` |
| reward = α · norm(performance) − β · norm(price_cost) |
| ``` |
| ## 💻 Code |
| |
| The official implementation of **LLMRouter**, including router implementations, training and evaluation pipelines, and utilities for working with **xRouteBench**, is available on GitHub: |
| |
| 👉 **[ulab-uiuc/LLMRouter](https://github.com/ulab-uiuc/LLMRouter)** |
| |
| |
| ## Notes |
| |
| - Query embeddings (`.pt`) are not included; they can be regenerated from the `query` field with any sentence encoder. |
| - The memory scenarios use the RAG top-k=5 turn-pair context variant. |
| |
| ## 📚 Citation |
| |
| If you find xRouteBench useful for your research or projects, please cite it as: |
| |
| ```bibtex |
| @article{feng2026llmrouter, |
| title={LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers}, |
| author={Feng, Tao and Yu, Fangxu and Zhang, Haozhen and Dai, Zhongjie and Yuan, Liangqi and Lei, Zijie and Zhang, Weizhi and Zhu, Kunlun and Yue, Haodong and Xuan, Keyang and others}, |
| journal={arXiv preprint arXiv:2608.06867}, |
| year={2026} |
| } |
| |
