Cross-lingual QA: A Key to Unlocking In-context Cross-lingual Performance
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911956139507712 |
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| author | Kim, Sunkyoung Ki, Dayeon Kim, Yireun Lee, Jinsik |
| author_facet | Kim, Sunkyoung Ki, Dayeon Kim, Yireun Lee, Jinsik |
| contents | Multilingual large language models (MLLMs) have demonstrated significant cross-lingual capabilities through in-context learning. Existing approaches typically construct monolingual in-context examples, either in the source or target language. However, translating entire in-context examples into the target language might compromise contextual integrity and be costly in the case of long-context passages. To address this, we introduce Cross-lingual QA, a cross-lingual prompting method that translates only the question and answer parts, thus reducing translation costs. Experiments on four typologically diverse multilingual benchmarks show that Cross-lingual QA prompting effectively stimulates models to elicit their cross-lingual knowledge, outperforming prior monolingual prompting approaches. Furthermore, we show that prompting open-source MLLMs with cross-lingual in-context examples enhances performance as the model scale increases. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_15233 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Cross-lingual QA: A Key to Unlocking In-context Cross-lingual Performance Kim, Sunkyoung Ki, Dayeon Kim, Yireun Lee, Jinsik Computation and Language Artificial Intelligence Multilingual large language models (MLLMs) have demonstrated significant cross-lingual capabilities through in-context learning. Existing approaches typically construct monolingual in-context examples, either in the source or target language. However, translating entire in-context examples into the target language might compromise contextual integrity and be costly in the case of long-context passages. To address this, we introduce Cross-lingual QA, a cross-lingual prompting method that translates only the question and answer parts, thus reducing translation costs. Experiments on four typologically diverse multilingual benchmarks show that Cross-lingual QA prompting effectively stimulates models to elicit their cross-lingual knowledge, outperforming prior monolingual prompting approaches. Furthermore, we show that prompting open-source MLLMs with cross-lingual in-context examples enhances performance as the model scale increases. |
| title | Cross-lingual QA: A Key to Unlocking In-context Cross-lingual Performance |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2305.15233 |