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--- |
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license: cc-by-4.0 |
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dataset_info: null |
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configs: |
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- config_name: CulturalBench-Hard |
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default: true |
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data_files: |
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- split: test |
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path: CulturalBench-Hard.csv |
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- config_name: CulturalBench-Easy |
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data_files: |
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- split: test |
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path: CulturalBench-Easy.csv |
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size_categories: |
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- 1K<n<10K |
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pretty_name: CulturalBench |
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--- |
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# CulturalBench - a Robust, Diverse and Challenging Benchmark on Measuring the (Lack of) Cultural Knowledge of LLMs |
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## **📌 Resources:** [Paper](https://arxiv.org/pdf/2410.02677) | [Leaderboard](https://huggingface.co/spaces/kellycyy/CulturalBench) |
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## 📘 Description of CulturalBench |
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- 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. |
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- We evaluate models on two setups: CulturalBench-Easy and CulturalBench-Hard which share the same questions but asked differently. |
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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. |
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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. |
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- See details on CulturalBench paper at [https://arxiv.org/pdf/2410.02677](https://arxiv.org/pdf/2410.02677). |
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### 🌎 Country distribution |
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| Continent | Num of questions | Included Country/Region | |
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|-----------------------|------------------|----------------------------------------------------------------| |
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| North America | 27 | Canada; United States | |
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| South America | 150 | Argentina; Brazil; Chile; Mexico; Peru | |
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| East Europe | 115 | Czech Republic; Poland; Romania; Ukraine; Russia | |
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| South Europe | 76 | Spain; Italy | |
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| West Europe | 96 | France; Germany; Netherlands; United Kingdom | |
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| Africa | 134 | Egypt; Morocco; Nigeria; South Africa; Zimbabwe | |
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| Middle East/West Asia | 127 | Iran; Israel; Lebanon; Saudi Arabia; Turkey | |
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| South Asia | 106 | Bangladesh; India; Nepal; Pakistan | |
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| Southeast Asia | 159 | Indonesia; Malaysia; Philippines; Singapore; Thailand; Vietnam | |
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| East Asia | 211 | China; Hong Kong; Japan; South Korea; Taiwan | |
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| Oceania | 26 | Australia; New Zealand | |
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## 🥇 Leaderboard of CulturalBench |
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- 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). |
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- We find that LLMs are sensitive to such difference in setups (e.g., GPT-4o with 27.3% difference). |
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- 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%. |
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## 📖 Example of CulturalBench |
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- Examples of questions in two setups: |
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%3C!-- HTML_TAG_END --> |
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## 💻 How to load the datasets |
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``` |
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from datasets import load_dataset |
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ds_hard = load_dataset("kellycyy/CulturalBench", "CulturalBench-Hard") |
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ds_easy = load_dataset("kellycyy/CulturalBench", "CulturalBench-Easy") |
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``` |
