Fùxì Benchmark

Fùxì (负屃), a comprehensive benchmark that evaluates both understanding and generation capabilities across 21 diverse tasks.

Overview

Fùxì (负屃) is a comprehensive benchmark designed to evaluate large language models on their understanding and generation capabilities of classical Chinese culture and literature. The benchmark covers 21 diverse tasks across multiple domains including classical literature, poetry, idiom, traditional Chinese medicine (TCM), and more.

All task descriptions:

Task	Description	Type	Eval Metric	#
Ancient Chinese RC	Multiple choice question on ancient Chinese text reading comprehension	NLU	Acc	300
Idiom RC	Multiple choice question on idiom reading comprehension	NLU	Acc	1000
TCM Syndrome RC	Multiple choice question on TCM syndrome differentiation	NLU	Acc	1098
Loan Character QA	Loan character identification question answering	NLG	Acc	500
Allegorical Saying QA	Allegorical saying question answering, to complete the second half	NLG	Acc	553
Book Author	Given a book title, answer the author	NLG	Acc	384
Book Dynasty	Given a book title, answer the dynasty	NLG	Acc	372
Book Collection Classification	Classify book titles into 10 collections	NLG	Acc	532
Poetry Generation	Poetry creation according to topic words	NLG	Acc	300
Poetry Line Source Tracing	Identify the source of a poem line	NLG	Acc	400
Famous Quote Source Tracing	Identify the source of a classical Chinese quote	NLG	Acc	200
Idiom Source Tracing	Name the source book or essay of an idiom	NLG	Acc	962
Inverse Poetry Translation	Decipher the original poem from translated modern Chinese version	NLG	Acc	300
Poetry Translation	Translate a poem into modern Chinese	NLG	BLEU	300
Ancient Chinese Translation	Translate ancient Chinese text into modern Chinese (extracted from the erya dataset)	NLG	BLEU	1000
Poetry Appreciation	Analysis of imagery, style, sentiment in classical Chinese poetry	NLG	BLEU	109
Idiom Explanation	Idiom explanation	NLG	BLEU	1236
TCM QA	Answer questions given related TCM text	NLG	BLEU	740
Prescription Explanation	Prescription details for TCM	NLG	BLEU	404
Couplet Generation	Generate the second line based on the first line	NLG	Acc	500
Ci Generation	Write a poem following a specific ci pattern with tonal rules	NLG	Acc	300

Usage

For zero-shot evaluation:

CUDA_VISIBLE_DEVICES=0 sh ./eval_zeroshot.sh

For few-shot in-context evaluation:

CUDA_VISIBLE_DEVICES=0 sh ./eval_icl.sh

The LLM evaluator configuration is in function lacc_evaluation_api

Results

Zero-shot Evaluation Results

Model	ACRC	IRC	TCMSRC	ASQA	LCQA	BA	BD	BCC	PG	PLST	FQST	IST	IPT	PT	ACT	PA	IE	TCMQA	PE
gpt-4o-mini	50.67	77.60	79.23	6.69	10.00	1.04	16.67	78.20	2.00	3.75	16.50	1.25	3.67	5.61	19.77	5.87	1.78	31.93	55.32
gpt-4o	66.33	86.00	84.79	28.57	26.80	10.16	38.98	91.73	41.33	24.00	46.00	12.89	36.33	1.80	19.86	4.88	3.02	38.12	66.07
qwen-max	86.00	85.90	86.97	52.26	42.40	20.31	40.32	96.61	45.00	58.00	75.00	41.27	73.33	1.64	22.85	6.43	1.85	22.81	53.43
glm-4-plus	82.33	87.50	83.52	51.36	37.20	29.69	52.96	97.37	76.67	52.25	82.00	49.17	66.00	3.88	17.24	8.53	2.03	23.41	38.82
internlm25-7b	48.67	77.20	67.85	3.62	4.20	0.00	1.88	68.61	3.00	15.75	31.50	8.52	25.33	2.07	14.08	7.49	85.53	4.32	40.38
internlm25-20b	59.33	80.70	73.95	26.40	31.80	3.39	21.51	93.23	40.67	33.25	70.00	63.20	36.00	1.81	12.51	7.53	56.86	3.78	39.19
glm4-9b	63.67	77.80	76.05	2.17	5.60	1.30	14.78	73.12	2.67	8.00	28.50	3.64	25.00	9.38	18.06	7.01	1.92	26.36	56.79
llama31-8b	30.00	61.70	56.19	0.36	2.20	0.52	9.14	59.21	0.00	0.25	3.00	0.42	1.00	4.12	9.88	2.53	2.06	39.79	1.09
llama31-8b-chinese	37.00	67.50	56.01	0.00	0.60	0.00	2.69	72.37	0.33	0.25	2.50	0.10	1.00	7.32	13.72	6.21	1.07	25.29	55.33
qwen2-0.5b	34.33	46.20	28.05	0.18	0.80	0.26	3.76	12.78	1.00	1.25	2.50	0.83	13.67	4.51	14.51	2.37	2.71	31.14	1.29
Xunzi-qwen2-7b	45.00	62.30	54.37	12.48	2.20	0.00	5.65	19.55	28.00	5.75	26.00	23.91	11.67	1.06	1.15	3.93	20.38	2.55	22.10
qwen2-7b	77.00	79.80	82.88	5.24	3.40	1.04	8.87	74.44	0.00	13.00	22.00	2.18	21.67	1.63	15.97	6.75	1.36	27.46	47.84
qwen2.5-1.5b	46.67	71.70	71.13	2.17	5.40	2.08	17.20	59.77	24.67	11.00	14.50	8.21	30.33	6.91	17.47	2.66	2.61	43.64	1.10
qwen2.5-3b	57.67	77.60	76.23	18.44	12.80	3.39	18.01	83.83	28.00	18.75	41.00	8.94	41.67	7.78	20.47	6.05	1.45	27.84	61.68
qwen2.5-7b	71.33	83.20	83.33	22.78	18.60	3.39	20.97	92.67	29.67	28.00	44.50	18.81	41.67	7.64	19.15	6.72	1.74	23.66	55.40
qwen2.5-14b	78.67	82.50	83.15	34.72	29.60	5.47	38.44	92.67	62.67	38.00	54.00	30.25	57.33	9.28	18.21	6.67	2.35	24.95	62.65
qwen2.5-72b	83.00	85.80	86.89	35.44	31.60	14.32	43.28	93.80	48.00	46.00	61.00	42.72	77.67	3.46	24.99	7.31	2.46	22.36	41.40

Generation Task Results (Zero-shot vs 5-shot)

Model	Zero-shot CG	Zero-shot CiG	5-shot CG	5-shot CiG
gpt-4o-mini	79.00	2.33	71.80	10.00
gpt-4o	89.60	55.33	83.40	53.33
qwen-max	96.60	25.67	93.80	39.67
glm-4-plus	19.20	41.67	44.00	54.33
internlm25-7b	32.40	17.67	59.80	18.67
internlm25-20b	63.40	3.67	44.40	6.67
glm4-9b	69.20	3.67	59.80	7.67
llama31-8b	8.60	0.33	0.20	0.00
llama31-8b-chinese	44.80	0.67	8.40	1.00
qwen2-0.5b	32.60	0.00	8.00	0.33
Xunzi-qwen2-7b	0.00	0.00	0.00	0.00
qwen2-7b	62.6	7.67	36.20	10.67
qwen2.5-1.5b	62.60	20.00	65.80	36.67
qwen2.5-3b	56.60	31.00	12.80	13.00
qwen2.5-7b	28.00	42.33	71.00	44.67
qwen2.5-14b	37.60	34.67	44.00	24.33
qwen2.5-72b	65.40	60.67	85.80	53.67

Citation

@misc{zhao2025fuxibenchmarkevaluatinglanguage,
title={F\`ux\`i: A Benchmark for Evaluating Language Models on Ancient Chinese Text Understanding and Generation}, author={Shangqing Zhao and Yuhao Zhou and Yupei Ren and Zhe Chen and Chenghao Jia and Fang Zhe and Zhaogaung Long and Shu Liu and Man Lan},
year={2025},
eprint={2503.15837},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.15837}, }