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---
license: cc-by-4.0
dataset_info: null
configs:
- config_name: CulturalBench-Hard
default: true
data_files:
- split: test
path: CulturalBench-Hard.csv
- config_name: CulturalBench-Easy
data_files:
- split: test
path: CulturalBench-Easy.csv
size_categories:
- 1K<n<10K
pretty_name: CulturalBench
---
# CulturalBench - a Robust, Diverse and Challenging Benchmark on Measuring the (Lack of) Cultural Knowledge of LLMs
## **📌 Resources:** [Paper](https://arxiv.org/pdf/2410.02677) | [Leaderboard](https://huggingface.co/spaces/kellycyy/CulturalBench)
## 📘 Description of CulturalBench
- CulturalBench is a set of 1,227 human-written and human-verified questions for effectively assessing LLMs’ cultural knowledge, covering 45 global regions including the underrepresented ones like Bangladesh, Zimbabwe, and Peru.
- We evaluate models on two setups: CulturalBench-Easy and CulturalBench-Hard which share the same questions but asked differently.
1. CulturalBench-Easy: multiple-choice questions (Output: one out of four options i.e. A,B,C,D). Evaluate model accuracy at question level (i.e. per `question_idx`). There are 1,227 questions in total.
2. CulturalBench-Hard: binary (Output: one out of two possibilties i.e. True/False). Evaluate model accuracy at question level (i.e. per `question_idx`). There are 1,227x4=4908 binary judgements in total with 1,227 questions provided.
- See details on CulturalBench paper at [https://arxiv.org/pdf/2410.02677](https://arxiv.org/pdf/2410.02677).
### 🌎 Country distribution
| Continent | Num of questions | Included Country/Region |
|-----------------------|------------------|----------------------------------------------------------------|
| North America | 27 | Canada; United States |
| South America | 150 | Argentina; Brazil; Chile; Mexico; Peru |
| East Europe | 115 | Czech Republic; Poland; Romania; Ukraine; Russia |
| South Europe | 76 | Spain; Italy |
| West Europe | 96 | France; Germany; Netherlands; United Kingdom |
| Africa | 134 | Egypt; Morocco; Nigeria; South Africa; Zimbabwe |
| Middle East/West Asia | 127 | Iran; Israel; Lebanon; Saudi Arabia; Turkey |
| South Asia | 106 | Bangladesh; India; Nepal; Pakistan |
| Southeast Asia | 159 | Indonesia; Malaysia; Philippines; Singapore; Thailand; Vietnam |
| East Asia | 211 | China; Hong Kong; Japan; South Korea; Taiwan |
| Oceania | 26 | Australia; New Zealand |
## 🥇 Leaderboard of CulturalBench
- We evaluated 30 frontier LLMs (update: 2024-10-04 13:20:58) and hosted the leaderboard at [https://huggingface.co/spaces/kellycyy/CulturalBench](https://huggingface.co/spaces/kellycyy/CulturalBench).
- We find that LLMs are sensitive to such difference in setups (e.g., GPT-4o with 27.3% difference).
- Compared to human performance (92.6% accuracy), CULTURALBENCH-Hard is more challenging for frontier LLMs with the best performing model (GPT-4o) at only 61.5% and the worst (Llama3-8b) at 21.4%.
## 📖 Example of CulturalBench
- Examples of questions in two setups:
## 💻 How to load the datasets
```
from datasets import load_dataset
ds_hard = load_dataset("kellycyy/CulturalBench", "CulturalBench-Hard")
ds_easy = load_dataset("kellycyy/CulturalBench", "CulturalBench-Easy")
``` |