Summary of Calmqa: Exploring Culturally Specific Long-form Question Answering Across 23 Languages, by Shane Arora et al.
CaLMQA: Exploring culturally specific long-form question answering across 23 languages
by Shane Arora, Marzena Karpinska, Hung-Ting Chen, Ipsita Bhattacharjee, Mohit Iyyer, Eunsol Choi
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces CaLMQA, a dataset for long-form question answering (LFQA) in 23 languages, with 1.5K complex culturally specific questions and 51 culturally agnostic questions translated from English. The dataset is designed to evaluate large language models’ ability to generate answers to complex questions in various languages. The authors collect naturally-occurring questions from community web forums and hire native speakers to write questions for under-resourced languages like Fijian and Kirundi. They automatically evaluate a suite of open- and closed-source models on CaLMQA, detecting incorrect language and token repetitions in answers. Their findings show that model performance degrades significantly for some low-resource languages. Human evaluation reveals that model performance is worse for culturally specific questions than for culturally agnostic questions. The study highlights the need for further research in non-English LFQA and provides an evaluation framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a big collection of questions in many languages to test how well computers can answer complex questions. It’s like asking Google or another smart computer program: “What is the main tradition in Fijian culture?” or “What is the law about marriage in Kirundi-speaking countries?” These questions are hard because they need answers that are a few sentences long, not just one word. The researchers made sure to include questions from all sorts of cultures and languages, even some that are hard to find information about. They tested how well some computer programs could answer these questions and found that the programs did worse than expected in some languages. This shows that we need more work on helping computers understand complex questions in many different languages. |
Keywords
» Artificial intelligence » Question answering » Token
