{"title":"WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild","authors":"Yuntian Deng, Wenting Zhao, Jack Hessel, Xiang Ren, Claire Cardie, Yejin Choi","doi":"arxiv-2409.03753","DOIUrl":null,"url":null,"abstract":"The increasing availability of real-world conversation data offers exciting\nopportunities for researchers to study user-chatbot interactions. However, the\nsheer volume of this data makes manually examining individual conversations\nimpractical. To overcome this challenge, we introduce WildVis, an interactive\ntool that enables fast, versatile, and large-scale conversation analysis.\nWildVis provides search and visualization capabilities in the text and\nembedding spaces based on a list of criteria. To manage million-scale datasets,\nwe implemented optimizations including search index construction, embedding\nprecomputation and compression, and caching to ensure responsive user\ninteractions within seconds. We demonstrate WildVis's utility through three\ncase studies: facilitating chatbot misuse research, visualizing and comparing\ntopic distributions across datasets, and characterizing user-specific\nconversation patterns. WildVis is open-source and designed to be extendable,\nsupporting additional datasets and customized search and visualization\nfunctionalities.","PeriodicalId":501281,"journal":{"name":"arXiv - CS - Information Retrieval","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Information Retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.03753","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The increasing availability of real-world conversation data offers exciting
opportunities for researchers to study user-chatbot interactions. However, the
sheer volume of this data makes manually examining individual conversations
impractical. To overcome this challenge, we introduce WildVis, an interactive
tool that enables fast, versatile, and large-scale conversation analysis.
WildVis provides search and visualization capabilities in the text and
embedding spaces based on a list of criteria. To manage million-scale datasets,
we implemented optimizations including search index construction, embedding
precomputation and compression, and caching to ensure responsive user
interactions within seconds. We demonstrate WildVis's utility through three
case studies: facilitating chatbot misuse research, visualizing and comparing
topic distributions across datasets, and characterizing user-specific
conversation patterns. WildVis is open-source and designed to be extendable,
supporting additional datasets and customized search and visualization
functionalities.