Summary of Culturalbench: a Robust, Diverse and Challenging Benchmark on Measuring the (lack Of) Cultural Knowledge Of Llms, by Yu Ying Chiu et al.
CulturalBench: a Robust, Diverse and Challenging Benchmark on Measuring the (Lack of) Cultural Knowledge of LLMs
by Yu Ying Chiu, Liwei Jiang, Bill Yuchen Lin, Chan Young Park, Shuyue Stella Li, Sahithya Ravi, Mehar Bhatia, Maria Antoniak, Yulia Tsvetkov, Vered Shwartz, Yejin Choi
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large language models (LLMs) can be more helpful across diverse cultures if they have effective cultural knowledge benchmarks. A team introduces CulturalBench: 1,227 human-written and verified questions covering 45 global regions, including underrepresented ones like Bangladesh and Peru. The questions span 17 topics, from food preferences to greeting etiquettes. Evaluating models on two setups (CulturalBench-Easy and CulturalBench-Hard), researchers find that LLMs are sensitive to the difference in setups and struggle with tricky questions that have multiple correct answers. GPT-4o outperforms other models in most regions, but all models underperform on South America and Middle Eastern questions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be better at understanding different cultures if they have good tests. A new test called CulturalBench has 1,227 questions about different countries and topics. The questions are checked by people to make sure they’re correct. This helps us see how well the models do on these kinds of questions. The results show that the models are not very good at answering some questions, especially those with multiple answers. One model, GPT-4o, is better than others in most places, but all models struggle with questions about South America and the Middle East. |
Keywords
» Artificial intelligence » Gpt
