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<!DOCTYPE html>
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<h1 class="logo">
<img src="./assets/images/vitality-logo-light.png" class="img-center" width="200"
alt="vitaLITy logo" />
</h1>
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<div class="tagline">
<p>A Visual Analytics Framework for Promoting Serendipitous Discovery of Academic Literature</p>
<small>Georgia Tech, UNC Charlotte, Northwestern University, Emory University, University of
Nottingham</small>
</div><!--//tagline-->
<div class="cta-container">
<a class="btn btn-info btn-cta m-xs" href="http://github.com/vitality-vis" target="_blank"><i
class="fab fa-github"></i> View on Github</a>
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<section class="cards-section text-center">
<div class="container">
<div class="intro">
<h2>Hi, Welcome to the vitaLITy project!</h2>
<br/>
<p class="text-left">There are a few prominent practices for conducting reviews of academic
literature, including searching for specific keywords on Google Scholar or checking citations
from some initial seed paper(s). These approaches serve a critical purpose for academic
literature reviews, yet there remain challenges in identifying relevant literature when similar
work may utilize different terminology (e.g., mixed-initiative visual analytics papers may not
use the same terminology as papers on model-steering, yet the two topics are relevant to one
another). We built vitaLITy to help researchers perform academic literature reviews via
serendipitous discovery. So far, we have two major releases:</p>
<hr>
<div>
<h4><strong>vitaLITy 2</strong> - <small>[v2, 2024]</small></h4></h4>
<h5><i>Reviewing Academic Literature Using Large Language Models</i></h5>
<h6 class="text-secondary text-bold">Hongye An, Arpit Narechania, Emily Wall, Kai Xu</h6>
<br />
<!-- <img src="./assets/images/v2-overview.png" class="img-teaser img-center" style="width: 60% !important" alt="vitaLITy 2 Overview" /> -->
<div class="embed-responsive embed-responsive-16by9">
<iframe class="embed-responsive-item"
src="https://www.youtube.com/embed/Ngu_TrorL8U?rel=0&showinfo=0" frameborder="0"
allowfullscreen></iframe>
</div>
<br />
<div>
<a class="btn btn-primary btn-cta m-xs" href="https://arxiv.org/abs/2408.13450"
target="_blank" download><i class="fas fa-file-pdf"></i> Paper (IEEE VIS NLVIZ'24)</a>
<a class="btn btn-secondary btn-cta m-xs text-white"><i class="fas fa-database"></i>
Dataset of 66,692 articles (Coming Soon)</a>
<a class="btn btn-secondary btn-cta m-xs text-white" target="_blank"><i
class="fas fa-rocket"></i> Demo (Coming Soon)</a>
</div>
<br />
<p class="text-left">
vitaLITy 2 uses a Large Language Model or LLM-based approach to identify semantically
relevant literature in a textual embedding space. We include a corpus of 66,692 papers from
1970-2023 which are searchable through text embeddings created by three language models.
vitaLITy 2 contributes a novel Retrieval Augmented Generation (RAG) architecture and can be
interacted with through an LLM with augmented prompts, including summarization of a
collection of papers. vitaLITy 2 also provides a chat interface that allow users to perform
complex queries without learning any new programming language. This also enables users to
take advantage of the knowledge captured in the LLM from its enormous training corpus.
</p>
<div class="text-left">
<p><strong>Citation:</strong></p>
<pre style="background: #efefef">
@misc{an2024vitality2,
title={vitaLITy 2: Reviewing Academic Literature Using Large Language Models},
author={Hongye An and Arpit Narechania and Emily Wall and Kai Xu},
year={2024},
eprint={2408.13450},
archivePrefix={arXiv},
primaryClass={cs.HC},
url={https://arxiv.org/abs/2408.13450},
howpublished={Presented at the NLVIZ Workshop, IEEE VIS 2024}
}
</pre>
</div>
</div>
<hr>
<div>
<h4><strong>vitaLITy</strong> - <small>[v1, 2021]</small></h4>
<h5><i>Promoting Serendipitous Discovery of Academic Literature with Transformers & Visual Analytics</i></h5>
<h6 class="text-secondary text-bold">Arpit Narechania, Alireza Karduni, Ryan Wesslen, Emily Wall</h6>
<!-- <img src="./assets/images/overview.png" class="img-teaser img-center" alt="vitaLITy Overview" /> -->
<div class="embed-responsive embed-responsive-16by9">
<iframe class="embed-responsive-item"
src="https://www.youtube.com/embed/fSgxSL4txzA?rel=0&showinfo=0" frameborder="0"
allowfullscreen></iframe>
</div>
<br />
<div>
<a class="btn btn-primary btn-cta m-xs"
href="https://arpitnarechania.github.io/docs/publications/vitality_vis_2021.pdf"
target="_blank" download><i class="fas fa-file-pdf"></i> Paper (TVCG'22)</a>
<a class="btn btn-primary btn-cta m-xs"
href="https://figshare.com/articles/dataset/VitaLITy_A_Dataset_of_Academic_Articles/14329151"
target="_blank"><i class="fas fa-database"></i> Dataset of 59,232 articles</a>
<a class="btn btn-primary btn-cta m-xs"
href="https://arpitnarechania.github.io/docs/posters/vitality_cridc_2022.pdf"
target="_blank" download><i class="fas fa-image"></i> Poster (CRIDC'22)</a>
<a class="btn btn-orange btn-cta m-xs" href="http://vitality.mathcs.emory.edu/app"
target="_blank"><i class="fas fa-rocket"></i> Demo</a>
</div>
<br />
<p class="text-left"><strong>vitaLITy</strong> promotes serendipitous discovery of relevant
literature using transformer language models, allowing users to find semantically similar
papers in a word embedding space given (1) a list of input paper(s) or (2) a working
abstract. vitaLITy visualizes this document-level embedding space in an interactive 2-D
scatterplot using dimension reduction. vitaLITy also summarizes meta information about the
document corpus or search query, including
keywords and co-authors, and allows users to save and export papers for use in a literature
review. We present qualitative findings from an evaluation of vitaLITy, suggesting it can be
a
promising complementary technique for conducting academic literature reviews. Furthermore,
we
contribute data from 38 popular data visualization publication venues in vitaLITy, and we
provide scrapers for the open-source community to continue to grow the list of supported
venues.
</p>
<div class="text-left">
<p><strong>Citation (Paper):</strong></p>
<pre style="background: #efefef">
@article{narechania2021vitality,
title={{vitaLITy: Promoting Serendipitous Discovery of Academic Literature with Transformers \& Visual Analytics}},
author={Narechania, Arpit and Karduni, Alireza and Wesslen, Ryan and Wall, Emily},
journal={IEEE TVCG},
year={{2022}},
url = {https://doi.org/10.1109/TVCG.2021.3114820},
publisher={IEEE}
}
</pre>
</div>
<br/>
<div class="text-left">
<p><strong>Citation (Dataset):</strong></p>
<pre style="background: #efefef">
@inproceedings{narechania2021vitalitydataset,
title={{VitaLITy: A Dataset of Academic Articles}},
journal={figshare},
booktitle={figshare},
publisher={figshare},
author={Narechania, Arpit and Karduni, Alireza and Wesslen, Ryan and Wall, Emily},
url={https://figshare.com/articles/dataset/VitaLITy_A_Dataset_of_Academic_Articles/14329151},
year={{2021}}
}
</pre>
</div>
</div>
</div><!--//intro-->
<hr>
<h2 class="title">It is easy to install / develop / extend! Checkout the underlying repositories!</h2>
<br />
<div class="cards-wrapper row">
<div class="item item-purple col-lg-6 col-6">
<div class="item-inner">
<div class="icon-holder">
<i class="icon icon_cogs"></i>
</div><!--//icon-holder-->
<h3 class="title">scraper: Academic Articles Web Scraper</h3>
<p class="intro">Leverage and contribute to our Python scraper that scrapes metadata from
digital libraries (e.g., ACM Digital Library)</p>
<a class="link" target="_blank"
href="https://github.com/vitality-vis/scraper"><span></span></a>
</div><!--//item-inner-->
</div><!--//item-->
<div class="item item-pink col-lg-6 col-6">
<div class="item-inner">
<div class="icon-holder">
<span aria-hidden="true" class="icon icon_key"></span>
</div><!--//icon-holder-->
<h3 class="title">embed: Generate Document Embeddings</h3>
<p class="intro">Utilize our Embed API to generate gloVe, Specter, ADA embeddings for input
documents.</p>
<a class="link" target="_blank"
href="https://github.com/vitality-vis/embed"><span></span></a>
</div><!--//item-inner-->
</div><!--//item-->
</div><!--//cards-->
<div class="cards-wrapper row">
<div class="item item-blue col-lg-6 col-6">
<div class="item-inner">
<div class="icon-holder">
<span aria-hidden="true" class="icon icon_cloud"></span>
</div><!--//icon-holder-->
<h3 class="title">rest-api: RESTful API for Similarity Search</h3>
<p class="intro">Utilize our RESTful API to query for similar papers by a list of seed
papers or a working abstract.</p>
<a class="link" target="_blank"
href="https://github.com/vitality-vis/rest-api"><span></span></a>
</div><!--//item-inner-->
</div><!--//item-->
<div class="item item-green col-lg-6 col-6">
<div class="item-inner">
<div class="icon-holder">
<span aria-hidden="true" class="icon icon_datareport_alt"></span>
</div><!--//icon-holder-->
<h3 class="title">frontend: Interactive Visualization</h3>
<p class="intro">Explore the document corpus of academic articles in our scalable,
interactive UI.</p>
<a class="link" target="_blank"
href="https://github.com/vitality-vis/frontend"><span></span></a>
</div><!--//item-inner-->
</div><!--//item-->
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<p>
vitaLITy: A Visual Analytics Framework for Promoting Serendipitous Discovery of Academic
Literature<br />
<small>Georgia Institute of Technology, UNC Charlotte, Northwestern University,
Emory University, University of Nottingham © 2024</small>
</p>
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