Summary of Bmike-53: Investigating Cross-lingual Knowledge Editing with In-context Learning, by Ercong Nie et al.
BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning
by Ercong Nie, Bo Shao, Zifeng Ding, Mingyang Wang, Helmut Schmid, Hinrich Schütze
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 BMIKE-53, a benchmark for cross-lingual in-context knowledge editing (IKE) across 53 languages. It combines three KE datasets: zsRE, CounterFact, and WikiFactDiff. The authors evaluate IKE under zero-shot, one-shot, and few-shot setups, using tailored metric-specific demonstrations. They find that model scale and demonstration alignment are crucial for cross-lingual IKE efficacy, with larger models and tailored demonstrations improving performance. Linguistic properties, such as script type, influence performance variation across languages, with non-Latin languages underperforming due to language confusion. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes a comprehensive benchmark for editing knowledge in different languages. It combines three datasets to test how well AI models can edit information from one language and use it in another language without getting confused. The authors tested the models in different situations, like not seeing any examples or just seeing one example. They found that making the model bigger and using specific demonstrations helps a lot. Language characteristics, such as script type, affect how well the models do in different languages, with some languages doing worse than others due to language confusion. |
Keywords
» Artificial intelligence » Alignment » Few shot » One shot » Zero shot
