{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T15:43:06Z","timestamp":1696088586389},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684369","type":"print"},{"value":"9781643684376","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,28]]},"abstract":"<jats:p>In this study, we delve into the \u201cshort circuit\u201d phenomenon observed in multiple-choice natural language reasoning tasks, where models tend to make accurate choices without properly considering the context of the question. To better understand this phenomenon, we propose white-box and black-box proxy tests as investigative tools to detect short circuit behavior, confirming its presence in fine-tuned NLU reasoning models. To tackle the short circuit issue, we introduce biologically inspired \u201ccrossover\u201d and \u201cmutation\u201d operations. These operations are applied to augment the training data for popular models such as BERT, XLNet, and RoBERTa. Our results demonstrate that these data augmentation techniques effectively enhance the models\u2019 robustness and mitigate the short circuit problem.<\/jats:p>","DOI":"10.3233\/faia230383","type":"book-chapter","created":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:11:59Z","timestamp":1695978719000},"source":"Crossref","is-referenced-by-count":0,"title":["Combating Short Circuit Behavior in Natural Language Reasoning: Crossover and Mutation Operations for Enhanced Robustness"],"prefix":"10.3233","author":[{"given":"Shanshan","family":"Huang","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siyu","family":"Ren","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenny Q.","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2023"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA230383","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,29]],"date-time":"2023-09-29T09:12:00Z","timestamp":1695978720000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA230383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"ISBN":["9781643684369","9781643684376"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia230383","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}