README.md

---
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)
```

## 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.