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    <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’ 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.</abstract>
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%0 Conference Proceedings
%T WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild
%A Deng, Yuntian
%A Zhao, Wenting
%A Hessel, Jack
%A Ren, Xiang
%A Cardie, Claire
%A Choi, Yejin
%Y Hernandez Farias, Delia Irazu
%Y Hope, Tom
%Y Li, Manling
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F deng-etal-2024-wildvis
%X 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’ 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.
%U https://aclanthology.org/2024.emnlp-demo.50
%P 497-506

Markdown (Informal)

[WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild](https://aclanthology.org/2024.emnlp-demo.50) (Deng et al., EMNLP 2024)

• WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild (Deng et al., EMNLP 2024)

ACL

• Yuntian Deng, Wenting Zhao, Jack Hessel, Xiang Ren, Claire Cardie, and Yejin Choi. 2024. WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 497–506, Miami, Florida, USA. Association for Computational Linguistics.