Papers with K-shot learning scenarios
Revisiting Automated Prompting: Are We Actually Doing Better? (2023.acl-short)
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| Challenge: | Recent work demonstrates that Large Language Models are great few-shot learners, and prompting significantly increases their performance on a range of downstream tasks. |
| Approach: | They revisit techniques for automated prompting on six different downstream tasks and a larger range of K-shot learning settings. |
| Outcome: | The proposed approach outperforms manual prompting on six different downstream tasks and a larger range of K-shot learning settings. |