Analyzing Context Contributions in LLM-based Machine Translation (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have achieved state-of-the-art performance in machine translation . however, the mechanisms by which LLMs use different parts of the input context remain unexplored . |
| Approach: | They propose to analyze how large language models use different parts of the input context . they highlight several key findings: the source part of few-shot examples contributes more than its corresponding targets . |
| Outcome: | The proposed model can leverage in-context learning to perform translation tasks without training . the proposed model is able to perform tasks without being explicitly trained for them . |
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