In-Context Demonstration Selection with Cross Entropy Difference (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) can use in-context demonstrations to improve performance on zero-shot tasks. |
| Approach: | They propose a cross-entropy difference method for selecting in-context demonstrations that uses parameter efficient finetuning to train small models on training data. |
| Outcome: | The proposed method outperforms baseline selection methods on a mix-domain dataset and shows that the effectiveness of in-context demonstrations negatively correlates with the perplexity of the test example. |
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| Challenge: | Recent learning-based demonstration selection methods have proven beneficial to in-context learning (ICL) by choosing more useful exemplars. |
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| Challenge: | In-context learning (ICL) heavily relies on selecting effective demonstrations to achieve outputs that better align with the expected results. |
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| Challenge: | In-context learning (ICL) enables large language models to perform tasks with only a few examples as demonstrations. |
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| Challenge: | Large Language Models (LLMs) have transformed NLP with their remarkable In-context Learning capabilities. |
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