metadata
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 | Leaderboard
📘 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.
- 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. - 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.
- 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
See details on CulturalBench paper at 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.
- 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")