Papers with Unified Demonstration Retriever

1 papers
Unified Demonstration Retriever for In-Context Learning (2023.acl-long)

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Challenge: In-context learning is a new learning paradigm where a language model conditions on a few input-output pairs (demonstrations) and a test input, and directly outputs the prediction.
Approach: They propose a single model to retrieve demonstrations for a wide range of tasks by combining training signals from various tasks into a unified list-wise ranking formulation by language model’s feedback.
Outcome: The proposed model outperforms baselines on 30+ tasks across 13 task families and multiple data domains.

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