Papers with multi-hop path reranking

1 papers
Few-shot Reranking for Multi-hop QA via Language Model Prompting (2023.acl-long)

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Challenge: Existing methods for multi-hop QA with open-domain questions require a large number of labeled question-document pairs for retrieval.
Approach: They propose a language-based prompt for multi-hop path reranking that relies on language model prompting to generate a relevance score between a question and the path.
Outcome: The proposed method yields strong retrieval performance on HotpotQA with only 128 training examples compared to state-of-the-art methods trained on thousands of examples.

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