@inproceedings{misra-etal-2023-triggering,
title = "Triggering Multi-Hop Reasoning for Question Answering in Language Models using Soft Prompts and Random Walks",
author = "Misra, Kanishka and
Nogueira dos Santos, Cicero and
Shakeri, Siamak",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.62",
doi = "10.18653/v1/2023.findings-acl.62",
pages = "972--985",
abstract = "Despite readily memorizing world knowledge about entities, pre-trained language models (LMs) struggle to compose together two or more facts to perform multi-hop reasoning in question-answering tasks. In this work, we propose techniques that improve upon this limitation by relying on random-walks over structured knowledge graphs. Specifically, we use soft-prompts to guide LMs to chain together their encoded knowledge by learning to map multi-hop questions to random-walk paths that lead to the answer. Applying our methods on two T5 LMs shows substantial improvements over standard tuning approaches in answering questions that require multi-hop reasoning.",
}
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<abstract>Despite readily memorizing world knowledge about entities, pre-trained language models (LMs) struggle to compose together two or more facts to perform multi-hop reasoning in question-answering tasks. In this work, we propose techniques that improve upon this limitation by relying on random-walks over structured knowledge graphs. Specifically, we use soft-prompts to guide LMs to chain together their encoded knowledge by learning to map multi-hop questions to random-walk paths that lead to the answer. Applying our methods on two T5 LMs shows substantial improvements over standard tuning approaches in answering questions that require multi-hop reasoning.</abstract>
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%0 Conference Proceedings
%T Triggering Multi-Hop Reasoning for Question Answering in Language Models using Soft Prompts and Random Walks
%A Misra, Kanishka
%A Nogueira dos Santos, Cicero
%A Shakeri, Siamak
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F misra-etal-2023-triggering
%X Despite readily memorizing world knowledge about entities, pre-trained language models (LMs) struggle to compose together two or more facts to perform multi-hop reasoning in question-answering tasks. In this work, we propose techniques that improve upon this limitation by relying on random-walks over structured knowledge graphs. Specifically, we use soft-prompts to guide LMs to chain together their encoded knowledge by learning to map multi-hop questions to random-walk paths that lead to the answer. Applying our methods on two T5 LMs shows substantial improvements over standard tuning approaches in answering questions that require multi-hop reasoning.
%R 10.18653/v1/2023.findings-acl.62
%U https://aclanthology.org/2023.findings-acl.62
%U https://doi.org/10.18653/v1/2023.findings-acl.62
%P 972-985
Markdown (Informal)
[Triggering Multi-Hop Reasoning for Question Answering in Language Models using Soft Prompts and Random Walks](https://aclanthology.org/2023.findings-acl.62) (Misra et al., Findings 2023)
ACL