@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://preview.aclanthology.org/jlcl-multiple-ingestion/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."
}