%0 Generic %1 yu2022retrieval %A Yu, Wenhao %A Zhu, Chenguang %A Zhang, Zhihan %A Wang, Shuohang %A Zhang, Zhuosheng %A Fang, Yuwei %A Jiang, Meng %D 2022 %K llm retrieval %T Retrieval Augmentation for Commonsense Reasoning: A Unified Approach %U http://arxiv.org/abs/2210.12887 %X A common thread of retrieval-augmented methods in the existing literature focuses on retrieving encyclopedic knowledge, such as Wikipedia, which facilitates well-defined entity and relation spaces that can be modeled. However, applying such methods to commonsense reasoning tasks faces two unique challenges, i.e., the lack of a general large-scale corpus for retrieval and a corresponding effective commonsense retriever. In this paper, we systematically investigate how to leverage commonsense knowledge retrieval to improve commonsense reasoning tasks. We proposed a unified framework of retrieval-augmented commonsense reasoning (called RACo), including a newly constructed commonsense corpus with over 20 million documents and novel strategies for training a commonsense retriever. We conducted experiments on four different commonsense reasoning tasks. Extensive evaluation results showed that our proposed RACo can significantly outperform other knowledge-enhanced method counterparts, achieving new SoTA performance on the CommonGen and CREAK leaderboards.

@misc{yu2022retrieval, abstract = {A common thread of retrieval-augmented methods in the existing literature focuses on retrieving encyclopedic knowledge, such as Wikipedia, which facilitates well-defined entity and relation spaces that can be modeled. However, applying such methods to commonsense reasoning tasks faces two unique challenges, i.e., the lack of a general large-scale corpus for retrieval and a corresponding effective commonsense retriever. In this paper, we systematically investigate how to leverage commonsense knowledge retrieval to improve commonsense reasoning tasks. We proposed a unified framework of retrieval-augmented commonsense reasoning (called RACo), including a newly constructed commonsense corpus with over 20 million documents and novel strategies for training a commonsense retriever. We conducted experiments on four different commonsense reasoning tasks. Extensive evaluation results showed that our proposed RACo can significantly outperform other knowledge-enhanced method counterparts, achieving new SoTA performance on the CommonGen and CREAK leaderboards.}, added-at = {2023-08-17T15:01:44.000+0200}, author = {Yu, Wenhao and Zhu, Chenguang and Zhang, Zhihan and Wang, Shuohang and Zhang, Zhuosheng and Fang, Yuwei and Jiang, Meng}, biburl = {https://www.bibsonomy.org/bibtex/251083bf5538e34f78740fefbd2530a56/lisa-ee}, description = {Retrieval Augmentation for Commonsense Reasoning: A Unified Approach}, interhash = {f60c8f62a844894d8d272d9b467b3455}, intrahash = {51083bf5538e34f78740fefbd2530a56}, keywords = {llm retrieval}, note = {cite arxiv:2210.12887Comment: EMNLP 2022 (main)}, timestamp = {2023-08-17T15:01:44.000+0200}, title = {Retrieval Augmentation for Commonsense Reasoning: A Unified Approach}, url = {http://arxiv.org/abs/2210.12887}, year = 2022 }