@inproceedings{khalifa-etal-2023-shot,
title = "Few-shot Reranking for Multi-hop {QA} via Language Model Prompting",
author = "Khalifa, Muhammad and
Logeswaran, Lajanugen and
Lee, Moontae and
Lee, Honglak and
Wang, Lu",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.885",
doi = "10.18653/v1/2023.acl-long.885",
pages = "15882--15897",
abstract = "We study few-shot reranking for multi-hop QA (MQA) with open-domain questions. To alleviate the need for a large number of labeled question-document pairs for retriever training, we propose PromptRank, which relies on language model prompting for multi-hop path reranking. PromptRank first constructs an instruction-based prompt that includes a candidate document path and then computes the relevance score between a given question and the path based on the conditional likelihood of the question given the path prompt according to a language model. PromptRank yields strong retrieval performance on HotpotQA with only 128 training examples compared to state-of-the-art methods trained on thousands of examples {---} 73.6 recall@10 by PromptRank vs. 77.8 by PathRetriever and 77.5 by multi-hop dense retrieval.",
}
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<abstract>We study few-shot reranking for multi-hop QA (MQA) with open-domain questions. To alleviate the need for a large number of labeled question-document pairs for retriever training, we propose PromptRank, which relies on language model prompting for multi-hop path reranking. PromptRank first constructs an instruction-based prompt that includes a candidate document path and then computes the relevance score between a given question and the path based on the conditional likelihood of the question given the path prompt according to a language model. PromptRank yields strong retrieval performance on HotpotQA with only 128 training examples compared to state-of-the-art methods trained on thousands of examples — 73.6 recall@10 by PromptRank vs. 77.8 by PathRetriever and 77.5 by multi-hop dense retrieval.</abstract>
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%0 Conference Proceedings
%T Few-shot Reranking for Multi-hop QA via Language Model Prompting
%A Khalifa, Muhammad
%A Logeswaran, Lajanugen
%A Lee, Moontae
%A Lee, Honglak
%A Wang, Lu
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F khalifa-etal-2023-shot
%X We study few-shot reranking for multi-hop QA (MQA) with open-domain questions. To alleviate the need for a large number of labeled question-document pairs for retriever training, we propose PromptRank, which relies on language model prompting for multi-hop path reranking. PromptRank first constructs an instruction-based prompt that includes a candidate document path and then computes the relevance score between a given question and the path based on the conditional likelihood of the question given the path prompt according to a language model. PromptRank yields strong retrieval performance on HotpotQA with only 128 training examples compared to state-of-the-art methods trained on thousands of examples — 73.6 recall@10 by PromptRank vs. 77.8 by PathRetriever and 77.5 by multi-hop dense retrieval.
%R 10.18653/v1/2023.acl-long.885
%U https://aclanthology.org/2023.acl-long.885
%U https://doi.org/10.18653/v1/2023.acl-long.885
%P 15882-15897
Markdown (Informal)
[Few-shot Reranking for Multi-hop QA via Language Model Prompting](https://aclanthology.org/2023.acl-long.885) (Khalifa et al., ACL 2023)
ACL
- Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, and Lu Wang. 2023. Few-shot Reranking for Multi-hop QA via Language Model Prompting. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 15882–15897, Toronto, Canada. Association for Computational Linguistics.