Datasets:
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---
task_categories:
- question-answering
- summarization
language:
- en
tags:
- numeric
- arithmetic
- math
pretty_name: NumericBench
size_categories:
- 10K<n<100K
configs:
- config_name: arithmetic_operation
data_files: "arithmetic_operation/*.json"
- config_name: mixed_number_sting
data_files: "mixed_number_sting/*.json"
- config_name: num_list
data_files: "num_list/*.json"
- config_name: sequence
data_files: "sequence/*.json"
- config_name: stock-single-trun
data_files: "stock/single-turn/*.json"
- config_name: stock-multi-turn
data_files: "stock/multi-turn/*.json"
- config_name: weather-single-turn
data_files: "weather/single-turn/*.json"
- config_name: weather-multi-turn
data_files: "weather/multi-turn/*.json"
---
# Introduction
**NumericBench** is a comprehensive benchmark designed to evaluate the numerical reasoning capabilities of Large Language Models, addressing their limitations in tasks like arithmetic, number recognition, contextual retrieval, comparison, summarization, and logical reasoning. By incorporating diverse datasets ranging from synthetic number lists to real-world domains like stock trends and weather patterns, NumericBench systematically tests LLMs in both structured and noisy contexts. Experiments on models such as GPT-4o and DeepSeek-V3 reveal significant weaknesses, emphasizing the need for numerically-aware modeling to enhance LLMs' real-world applicability.
Github Repo: https://github.com/TreeAI-Lab/NumericBench
# How to use it?
## Loading Data
``` python
from huggingface_hub import hf_hub_download
import pandas as pd
dataset_name_list = ['arithmetic_operation/context_arithmetic_operation.json', 'arithmetic_operation/arithmetic_operation.json',
'mixed_number_sting/mixed_number_string_500_per_sample.json',
'num_list/num_list_500_per_sample_1000_length.json', 'num_list/num_list_500_per_sample_100_length.json',
'sequence/sequence_500_sample_100_length.json',
'stock/single-turn/stock_500_per_sample_150_length.json', 'stock/single-turn/stock_500_per_sample_300_length.json', 'stock/multi-turn/stock_multi_turn_100_per_sample_100_length.json',
'weather/single-turn/weather_500_per_sample_200_length.json', 'weather/single-turn/weather_500_per_sample_400_length.json', 'weather/multi-turn/weather_multi_turn_100_per_sample_100_length.json'
]
REPO_ID = "TreeAILab/NumericBench"
for dataset_name in dataset_name_list:
dataset = pd.read_json(
hf_hub_download(repo_id=REPO_ID, filename=dataset_name, repo_type="dataset")
)
```
Alternatively, you can download the dataset from [this link](https://zenodo.org/records/14875784?token=eyJhbGciOiJIUzUxMiJ9.eyJpZCI6IjZhYjZlYzYwLWZkMTgtNGU1Ni1iM2I2LWUwNWVlMGIwZmYwZCIsImRhdGEiOnt9LCJyYW5kb20iOiIwNzQxNmU0NWY5ZDkxMzQ4ODVmYjdlZDgyOGJmNjVhYSJ9.m-_8Owb3TohJ76cGt2Mu4wrpsxMgC4E_aJG7Q07KHOTlKaxB4kipMY3eBZPaQEIkOv_iJEkRmlvZr23rLkTMBw).
## Data Format
Due to the excessive length of the content, "..." is used to indicate omission.
### single-turn
``` json
{
"system_prompt": "...",
"system_prompt_cot": "...",
"description": "...",
"data": [
{
"idx": 0,
"question_index": 0,
"question": "What is the result of A + B? Please round the answer to two decimal places. ",
"struct_data": "{'A': 6.755, 'B': -1.225}",
"answer": 5.53,
"ability": "1-digit integer with 3 decimal places"
},
{
"idx": 1,
"question_index": 1,
"question": "What is the result of A - B? Please round the answer to two decimal places. ",
"struct_data": "{'A': 6.755, 'B': -1.225}",
"answer": 7.98,
"ability": "1-digit integer with 3 decimal places"
}
]
}
```
### multi-turn
``` json
{
"system_prompt": "...",
"system_prompt_cot": "...",
"description": "...",
"data": [
{
"idx": 0,
"multi_turn_QA": [
{
"turn_index": 0,
"question_index": 6,
"struct_data": "...",
"question": "...",
"answer": "F",
"ability": "contextual retrieval"
}, ...
]
}, ...
]
}
```
## Evaluation
# Dataset statistics
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