metadata
language:
- zh
language_bcp47:
- zh-CN
license: cc-by-4.0
task_categories:
- text-classification
- feature-extraction
tags:
- benchmark
- calibration
- decision-models
- system-one
- chinese
- jev
- evaluation
size_categories:
- n<1k
configs:
- config_name: voice_routing
data_files:
- split: validation
path: voice_routing.jsonl
- config_name: business_scenarios
data_files:
- split: validation
path: business_scenarios.jsonl
zh-decision-bench (v0.2)
First Chinese-language calibration benchmark for Jev-class "System One" decision models — accuracy and probability calibration on Chinese tasks.
Full methodology, five-model results (Jev, NeoHorse-Jev-4B, Laya x2, Qwen3.5-2B), raw predictions and the human adjudication log live in the source repository. The eval set also ships in Laya's research/evals/ as of v0.3.21.
Configs
| config | items | questions | source |
|---|---|---|---|
voice_routing |
179 | 179 (choice, 6-way) | MASSIVE zh-CN dev (Amazon, CC BY 4.0), quality-filtered, fixed seed |
business_scenarios |
40 | 105 (choice/score/noul) | Synthetic: LLM-drafted, human-adjudicated (review log) |
Fields
state— the text to decide on (utterance / ticket / message)questions— question dict keyed by question id; each hastype(choice|score|noul),instructions,criteria(options: label->description; score: ordered level list; noul: true/false descriptions)gold— ground truth keyed by question id (label / level / boolean)domain,source,difficulty,tags,notes— provenance and slicing
Usage
from datasets import load_dataset
ds = load_dataset("CodyQin/zh-decision-bench", "voice_routing")
Changelog
- v0.2 (2026-09-28): voice_routing 179 -> 323 (same task, official labels); business_scenarios 40 -> 55 (human-adjudicated); five-model matrix incl. NeoHorse-Jev-4B; revised the v0.1 over-confidence reading at larger n.
- v0.1 (2026-09-27): initial release.
License
CC BY 4.0. MASSIVE-derived rows attribute Amazon MASSIVE (CC BY 4.0); synthetic rows are original.