Benchmark

LLM instruction-following evaluation service

The service provides comprehensive evaluation and customized solutions for LLMs, offering model performance assessment, data strategy consultation, tailored dataset creation, and cost-effective iteration management.

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LLM Benchmark Dataset Features

Model Target

Professional scene coverage

High Data Quality Standards

Evaluation-Algorithm Collaboration Iteration

Benchmark with custom datasets

Data-strategy-centered service dedicated to ensuring top-notch quality throughout the planning and production phases.

1. Data Production Plan

Strictly standardize data content based on model objectives to ensure data diversity and accuracy

2. Data Incentive Mechanism

Establish an incentive mechanism for data teams, match tasks with professional teams, and cover comprehensive data scenarios

4. Data Statistics Report

Data distribution statistics and Bad Case summaries, optimize data production plans, and achieve the highest data quality standards.

3. Data Strategy Training

Collaborate with data teams, refine data production processes, and strengthen data understanding

CIF-Bench

The first LLM instruction-following evaluation services in the world.

Evaluation system

Automated Evaluation Ranking

The evaluation of 28 selected LLMs models revealed significant performance gaps, highlighting the limitations of LLMs' generalization capabilities in Chinese task environments.

Model Name	Overall	Chinese Culture	Classification	Code	Commonsense	Creative NLG	Evaluation	Grammar	Linguistic	Motion Detection	NER	NLI	QA	Reasoning	Role Playing	Sentiment	Structured Data	Style Transfer	Summarization	Toxic	Translation


Baichuan2-13B-Chat	0.529	0.52	0.674	0.333	0.641	0.497	0.686	0.542	0.528	0.578	0.563	0.632	0.569	0.515	0.752	0.624	0.459	0.462	0.332	0.441	0.273
Qwen-72B-Chat	0.519	0.486	0.63	0.296	0.634	0.508	0.634	0.458	0.52	0.494	0.55	0.626	0.565	0.528	0.762	0.613	0.496	0.459	0.282	0.608	0.271
Yi-34B-Chat	0.512	0.483	0.606	0.347	0.623	0.497	0.598	0.48	0.49	0.575	0.525	0.619	0.554	0.494	0.757	0.58	0.472	0.439	0.346	0.514	0.259
Qwen-14B-Chat	0.5	0.481	0.582	0.307	0.614	0.494	0.645	0.428	0.475	0.496	0.513	0.616	0.548	0.507	0.764	0.583	0.469	0.453	0.283	0.575	0.262
Deepseek-Llm-67B-Chat	0.471	0.467	0.571	0.259	0.577	0.486	0.549	0.442	0.476	0.475	0.509	0.566	0.496	0.439	0.711	0.546	0.409	0.436	0.262	0.57	0.235
Baichuan-13B-Chat	0.45	0.408	0.491	0.286	0.552	0.439	0.67	0.417	0.422	0.482	0.486	0.565	0.505	0.377	0.704	0.552	0.387	0.402	0.35	0.431	0.304
Chatglm3-6B	0.436	0.381	0.439	0.33	0.541	0.452	0.577	0.31	0.358	0.436	0.453	0.544	0.503	0.414	0.762	0.56	0.446	0.402	0.321	0.391	0.27
Yi-6B-Chat	0.417	0.402	0.454	0.313	0.523	0.425	0.506	0.383	0.383	0.487	0.396	0.523	0.457	0.369	0.754	0.482	0.401	0.38	0.31	0.455	0.227
Baichuan2-7B-Chat	0.412	0.437	0.647	0.16	0.52	0.402	0.58	0.511	0.444	0.455	0.407	0.489	0.395	0.406	0.67	0.517	0.342	0.298	0.101	0.463	0.138
Chatglm2-6B	0.352	0.278	0.469	0.346	0.403	0.424	0.535	0.274	0.397	0.406	0.24	0.397	0.352	0.326	0.714	0.438	0.298	0.313	0.32	0.461	0.19
Chatglm-6B-Sft	0.349	0.265	0.454	0.365	0.385	0.462	0.554	0.296	0.379	0.427	0.232	0.38	0.321	0.292	0.718	0.415	0.296	0.333	0.351	0.441	0.19
Chinese-Llama2-Linly-13B	0.344	0.25	0.462	0.311	0.399	0.429	0.557	0.273	0.358	0.385	0.268	0.39	0.33	0.313	0.653	0.433	0.279	0.332	0.292	0.457	0.181
Gpt-3.5-Turbo-Sft	0.343	0.269	0.427	0.298	0.389	0.395	0.575	0.325	0.365	0.389	0.226	0.382	0.394	0.345	0.71	0.433	0.324	0.266	0.29	0.397	0.225
Chinese-Alpaca-2-13B	0.341	0.242	0.421	0.356	0.382	0.442	0.602	0.256	0.363	0.43	0.21	0.376	0.334	0.317	0.714	0.459	0.299	0.316	0.308	0.452	0.2
Chinese-Alpaca-13B	0.334	0.25	0.399	0.348	0.364	0.435	0.616	0.275	0.349	0.421	0.223	0.37	0.309	0.319	0.724	0.426	0.285	0.307	0.298	0.445	0.181
Chinese-Alpaca-7B	0.334	0.216	0.412	0.378	0.381	0.425	0.576	0.265	0.359	0.393	0.243	0.383	0.326	0.295	0.71	0.409	0.301	0.327	0.325	0.405	0.186
Chinese-Llama2-Linly-7B	0.333	0.218	0.451	0.33	0.396	0.427	0.583	0.248	0.35	0.41	0.231	0.367	0.345	0.276	0.698	0.433	0.259	0.315	0.31	0.469	0.168
Tigerbot-13B-Chat	0.331	0.205	0.397	0.309	0.385	0.42	0.614	0.31	0.379	0.341	0.276	0.363	0.329	0.301	0.694	0.419	0.28	0.31	0.283	0.393	0.186
Telechat-7B	0.329	0.267	0.338	0.321	0.42	0.404	0.42	0.272	0.265	0.327	0.32	0.388	0.355	0.244	0.672	0.344	0.334	0.335	0.299	0.364	0.184
Ziya-Llama-13B	0.329	0.196	0.402	0.324	0.341	0.428	0.616	0.312	0.349	0.4	0.228	0.351	0.279	0.313	0.721	0.468	0.311	0.291	0.278	0.431	0.175
Chinese-Alpaca-33B	0.326	0.234	0.37	0.372	0.364	0.429	0.614	0.246	0.318	0.377	0.221	0.368	0.3	0.314	0.713	0.428	0.288	0.303	0.295	0.401	0.199
Tigerbot-7B-Chat	0.325	0.218	0.395	0.306	0.37	0.413	0.631	0.294	0.37	0.368	0.215	0.355	0.313	0.292	0.713	0.415	0.283	0.315	0.29	0.389	0.171
Chinese-Alpaca-2-7B	0.323	0.215	0.374	0.335	0.366	0.415	0.546	0.257	0.326	0.395	0.215	0.375	0.318	0.289	0.698	0.417	0.285	0.303	0.312	0.439	0.193
Aquilachat-7B	0.309	0.162	0.234	0.291	0.32	0.437	0.344	0.135	0.266	0.309	0.287	0.337	0.342	0.236	0.609	0.255	0.249	0.4	0.527	0.43	0.306
Moss-Moon-003-Sft	0.302	0.214	0.405	0.274	0.347	0.38	0.448	0.305	0.341	0.378	0.232	0.317	0.321	0.267	0.694	0.375	0.251	0.259	0.288	0.424	0.152
Qwen-7B-Chat	0.301	0.211	0.41	0.289	0.349	0.391	0.531	0.219	0.387	0.404	0.208	0.325	0.297	0.278	0.681	0.419	0.266	0.251	0.248	0.371	0.157
Belle-13B-Sft	0.264	0.198	0.307	0.285	0.316	0.349	0.409	0.237	0.305	0.222	0.177	0.317	0.284	0.242	0.631	0.299	0.244	0.222	0.234	0.296	0.133
Cpm-Bee-10B	0.244	0.234	0.377	0.024	0.278	0.311	0.255	0.302	0.278	0.327	0.148	0.286	0.224	0.147	0.603	0.277	0.117	0.263	0.22	0.352	0.125

CIF-Bench Evaluation Service

We provide highly customizable evaluation services that seamlessly integrate with API or model code.

Unlimited Access to CIF-Bench Public Dataset

Including 45,000 data instances, the Chinese Instruction-Following Benchmark (CIF-Bench) enables the development of more adaptable, culturally aware, and linguistically diverse language models.

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Automated Evaluation Service

By providing the API or model code, users can quickly and easily receive performance feedback on their models using our comprehensive dataset for model inference evaluation.

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Manual Evaluation Service

Leveraging user-submitted interfaces, models, and code, our expert team conducts detailed tests to deliver a comprehensive report for you.

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