Data Analysis and Visualization

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Data Analysis and Visualization

Data Analysis and Visualization

@dbvis

The Data Analysis and Visualization Group (DBVIS) of Daniel A. Keim at the @UniKonstanz

University of Konstanz Sumali Ağustos 2009
136 Sinusundan4.6K Mga Tagasunod
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Data Analysis and Visualization
Data Analysis and Visualization@dbvis·
Neues coronavis.dbvis.de Feature 🎉 >Konfigurierbare Dashboards< Damit kannst du ganz einfach Dashboards mit vielen Charts erstellen um Informationen und Regionen zu vergleichen. Die Charts können gespeichert und per URL geteilt werden. Entwickelt an der @UniKonstanz
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Rita Sevastjanova
Rita Sevastjanova@RSevastjanova·
Our visualizations show that larger models, e.g., ChatGPT and Bard, are less biased than their smaller counterparts. Are the models getting better or the engineering steps used to adapt the outputs? 🤔@melassady #ieeevis #vds 👉prompt-comparison.lingvis.io
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John Alexis Guerra Gómez @johnguerra.bsky.social@duto_guerra

Very interesting work: Visual Comparison of Text Sequences Generated by Large Language Models R. Sevastjanova, S. Vogelbacher, A. Spitz, D. A. Keim, M. El-Assady #VDS Symposium @ieeevis Demo and paper 👉🏼prompt-comparison.lingvis.io

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Menna El-Assady
Menna El-Assady@melassady·
Thank you to #EuroVis and the Early Career Award Committee for the award. I'm honored and humbled to be working in, and recognized by the amazing #ieeevis community. Looking forward to many more years of #visualization research and future collaborations. ☺️ #eurovis2023
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Ben Shneiderman
Ben Shneiderman@benbendc·
Daniel Keim and Menna El-Assady of Univ of Konstanz offer an interactive exploration tool for a thoughtfully indexed set of 71 papers on Human-Centered Evaluations of Human-Centered Machine Learning …an-centered-evaluations-star.dbvis.de
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Universität Konstanz
Universität Konstanz@UniKonstanz·
Nach dem erfolgreichen Kick-Off im April '22 wurde das #UniKonstanz Centre for HUMAN | DATA | SOCIETY gegründet. Das Zentrum untersucht transdisziplinär Aspekte der Datengesellschaft sowie die Interaktionen zwischen Menschen + datengetriebenen Systemen. t1p.de/4lrpm
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Menna El-Assady
Menna El-Assady@melassady·
How should we compare the embedding spaces of large language models? Did language model adapters (@AdapterHub) introduce bias? Join us in the current #ieeevis session on comparison in Oklahoma Station 1. #vis22
Rita Sevastjanova@RSevastjanova

Our @ieeevis paper shows how to visually compare embedding spaces produced by adapted language models. Thanks, @AdapterHub, for the variety of adapters for evaluation!🥳And a big thank you to my co-authors E.Cakmak @ravfogel @ryandcotterell @melassady!😀 adapters.demo.lingvis.io

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Visual Musicology
Visual Musicology@vis_musicology·
We prepared all our research material as a Linktree to provide a concise overview of all our recent projects. 🚀🚀🚀Enjoy🚀🚀🚀 linktr.ee/visualmusicolo…
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Rita Sevastjanova
Rita Sevastjanova@RSevastjanova·
To "understand language", masked #languagemodels should capture semantic constraints of function words (e.g., negation, coordination, quantifiers). We show that BERT, RoBERTa, and ALBERT don't: arxiv.org/abs/2209.07836 More: function-words.lingvis.io To appear @coling2022 . @dbvis
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arXiv CS-CL@arxiv_cscl

Negation, Coordination, and Quantifiers in Contextualized Language Models arxiv.org/abs/2209.07836

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Rita Sevastjanova
Rita Sevastjanova@RSevastjanova·
What a cool summary of our recent paper on challenges that may occur when designing language model explanations! 😍 Thanks, @NLPParrot ! 😀 Paper: arxiv.org/abs/2207.06897 @manunna_91 @dbvis
NLPurr@NLPurr

As I mentioned before, I am really glad to be able to merge my love for NLP with that of sketchnoting for science communication. Today's paper is "Beware the Rationalization Trap!" by @RSevastjanova and @manunna_91 (1/7)

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Menna El-Assady
Menna El-Assady@melassady·
Happening now at #eurovis2022, @RSevastjanova presenting our work on Language Model Explainability (@LingVISio). Check out lmfingerprints.lingvis.io for more information. #NLProc
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Rita Sevastjanova@RSevastjanova

We use visual analytics to create insights into properties learned by transformer-based language models. Our scoring functions can be used as an interpretable alternative to probing classifiers😉 Check out: lmfingerprints.lingvis.io @manunna_91 @HannaJSchaefer @dbvis @EuroVisConf

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Tuklasin

@melassady @VisualDataSci @ieeevis @metz_yannick @AdapterHub @ravfogel @VisGuides @RSevastjanova