{"title":"粉红色:具有最小监督的预设常识推理","authors":"Ehsan Qasemi, Piyush Khanna, Qiang Ning, Muhao Chen","doi":"10.48550/arXiv.2206.07920","DOIUrl":null,"url":null,"abstract":"Reasoning with preconditions such as “glass can be used for drinking water unless the glass is shattered” remains an open problem for language models. The main challenge lies in the scarcity of preconditions data and the model’s lack of support for such reasoning. We present PInKS , Preconditioned Commonsense Inference with WeaK Supervision, an improved model for reasoning with preconditions through minimum supervision. We show, empirically and theoretically, that PInKS improves the results on benchmarks focused on reasoning with the preconditions of commonsense knowledge (up to 40% Macro-F1 scores). We further investigate PInKS through PAC-Bayesian informativeness analysis, precision measures, and ablation study.","PeriodicalId":39298,"journal":{"name":"AACL Bioflux","volume":"6 1","pages":"320-336"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"PInKS: Preconditioned Commonsense Inference with Minimal Supervision\",\"authors\":\"Ehsan Qasemi, Piyush Khanna, Qiang Ning, Muhao Chen\",\"doi\":\"10.48550/arXiv.2206.07920\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Reasoning with preconditions such as “glass can be used for drinking water unless the glass is shattered” remains an open problem for language models. The main challenge lies in the scarcity of preconditions data and the model’s lack of support for such reasoning. We present PInKS , Preconditioned Commonsense Inference with WeaK Supervision, an improved model for reasoning with preconditions through minimum supervision. We show, empirically and theoretically, that PInKS improves the results on benchmarks focused on reasoning with the preconditions of commonsense knowledge (up to 40% Macro-F1 scores). We further investigate PInKS through PAC-Bayesian informativeness analysis, precision measures, and ablation study.\",\"PeriodicalId\":39298,\"journal\":{\"name\":\"AACL Bioflux\",\"volume\":\"6 1\",\"pages\":\"320-336\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AACL Bioflux\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.48550/arXiv.2206.07920\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Environmental Science\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AACL Bioflux","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.48550/arXiv.2206.07920","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Environmental Science","Score":null,"Total":0}
引用次数: 6
摘要
对语言模型来说,用诸如“玻璃可以用来喝水,除非玻璃碎了”这样的前提条件进行推理仍然是一个有待解决的问题。主要的挑战在于先决条件数据的稀缺性和模型缺乏对这种推理的支持。我们提出了PInKS, Preconditioned Commonsense Inference with WeaK Supervision,这是一个通过最小监督进行有前提条件推理的改进模型。我们从经验和理论上证明,PInKS提高了以常识知识为前提的推理基准测试的结果(高达40%的Macro-F1分数)。我们通过PAC-Bayesian信息性分析、精度测量和消融研究进一步研究了PInKS。
PInKS: Preconditioned Commonsense Inference with Minimal Supervision
Reasoning with preconditions such as “glass can be used for drinking water unless the glass is shattered” remains an open problem for language models. The main challenge lies in the scarcity of preconditions data and the model’s lack of support for such reasoning. We present PInKS , Preconditioned Commonsense Inference with WeaK Supervision, an improved model for reasoning with preconditions through minimum supervision. We show, empirically and theoretically, that PInKS improves the results on benchmarks focused on reasoning with the preconditions of commonsense knowledge (up to 40% Macro-F1 scores). We further investigate PInKS through PAC-Bayesian informativeness analysis, precision measures, and ablation study.