Product and service differentiation by virtue of delivering a unique personalized consumer experience is considered by many as the next competitive battleground. Due to the e-commerce sales surge during the pandemic more often these days the consumers first tangible interaction with the brand is now upon receipt of a package purchased online. Brands look at the consumer touchpoints with the brand, its packaging and labels, as more and more critical in delivering and managing an event-based-experience. How you "engineer" your users to engage is the human factor/behavioral element of document engineering. This presentation will look at real-life examples of how brands are evolving their strategies when focussing on event based experiences to both deliver new brand marketing/consumer experience strategies and to create data sets from the consumer engagement to address both old-age business problems and challenges and some new emerging ones.
{"title":"The Evolution and Growth of Engineering Documents for Consumer Engagement","authors":"Gary Moloney","doi":"10.1145/3573128.3607807","DOIUrl":"https://doi.org/10.1145/3573128.3607807","url":null,"abstract":"Product and service differentiation by virtue of delivering a unique personalized consumer experience is considered by many as the next competitive battleground. Due to the e-commerce sales surge during the pandemic more often these days the consumers first tangible interaction with the brand is now upon receipt of a package purchased online. Brands look at the consumer touchpoints with the brand, its packaging and labels, as more and more critical in delivering and managing an event-based-experience. How you \"engineer\" your users to engage is the human factor/behavioral element of document engineering. This presentation will look at real-life examples of how brands are evolving their strategies when focussing on event based experiences to both deliver new brand marketing/consumer experience strategies and to create data sets from the consumer engagement to address both old-age business problems and challenges and some new emerging ones.","PeriodicalId":310776,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering 2023","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125469876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Hillebrand, Armin Berger, Tobias Deußer, Tim Dilmaghani, Mohamed Khaled, Bernd Kliem, Rüdiger Loitz, Maren Pielka, David Leonhard, C. Bauckhage, R. Sifa
Auditing financial documents is a very tedious and time-consuming process. As of today, it can already be simplified by employing AI-based solutions to recommend relevant text passages from a report for each legal requirement of rigorous accounting standards. However, these methods need to be fine-tuned regularly, and they require abundant annotated data, which is often lacking in industrial environments. Hence, we present ZeroShotALI, a novel recommender system that leverages a state-of-the-art large language model (LLM) in conjunction with a domain-specifically optimized transformer-based text-matching solution. We find that a two-step approach of first retrieving a number of best matching document sections per legal requirement with a custom BERT-based model and second filtering these selections using an LLM yields significant performance improvements over existing approaches.
{"title":"Improving Zero-Shot Text Matching for Financial Auditing with Large Language Models","authors":"L. Hillebrand, Armin Berger, Tobias Deußer, Tim Dilmaghani, Mohamed Khaled, Bernd Kliem, Rüdiger Loitz, Maren Pielka, David Leonhard, C. Bauckhage, R. Sifa","doi":"10.1145/3573128.3609344","DOIUrl":"https://doi.org/10.1145/3573128.3609344","url":null,"abstract":"Auditing financial documents is a very tedious and time-consuming process. As of today, it can already be simplified by employing AI-based solutions to recommend relevant text passages from a report for each legal requirement of rigorous accounting standards. However, these methods need to be fine-tuned regularly, and they require abundant annotated data, which is often lacking in industrial environments. Hence, we present ZeroShotALI, a novel recommender system that leverages a state-of-the-art large language model (LLM) in conjunction with a domain-specifically optimized transformer-based text-matching solution. We find that a two-step approach of first retrieving a number of best matching document sections per legal requirement with a custom BERT-based model and second filtering these selections using an LLM yields significant performance improvements over existing approaches.","PeriodicalId":310776,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering 2023","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127050041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the ACM Symposium on Document Engineering 2023","authors":"","doi":"10.1145/3573128","DOIUrl":"https://doi.org/10.1145/3573128","url":null,"abstract":"","PeriodicalId":310776,"journal":{"name":"Proceedings of the ACM Symposium on Document Engineering 2023","volume":"2016 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128035958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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