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21 changes: 10 additions & 11 deletions index.html
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<meta name="resource-type" content="document">
<meta name="distribution" content="global">
<meta name="KeyWords" content="Conference">
<title>LongEval 2024</title>
<title>LongEval 2025</title>
</head>

<body>

<div class="banner">
<img src="assets/banner.jpg" alt="Conference Template Banner">
<div class="top-left">
<span class="title1">LongEval CLEF 2024 Lab</span>
<span class="title1">LongEval CLEF 2025 Lab</span>
</div>
<div class="bottom-right">
Longitudinal Evaluation of Model Performance
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<a title="Submissions" href="submissions">Submissions</a>
</td>
<td class="navigation">
<a title="2024" href="https://clef-longeval.github.io/">2024</a>
<a title="2023" href="https://clef-longeval-2023.github.io/">2023</a>
</td>
</tr>
</table>

<h3><b>Stay tuned for LongEval 2025 announcements ! </b></h3>

<h2><b>Agenda for September 12 (all times EST)</b></h2>

<h2><b>Agenda for September XX (to change this section) (all times EST)</b></h2>
<p>
<table>
<tr>
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</tr>
<tr>
<td style="width: 100px;">12:25 - 12:45 </td>
<td>Team Galápagos Tortoise at LongEval 2024: Neural Re-Ranking and Rank Fusion for Temporal Stability
<td>Team Galápagos Tortoise at LongEval 2025: Neural Re-Ranking and Rank Fusion for Temporal Stability
<br>
Marlene Gründel, Malte Weber, Johannes Franke and Jan Heinrich Merker
</td>
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</p>

<h2>LongEval Lab</h2>
<p> In this page we present CLEF 2024 shared task evaluating the temporal persistence of information retrieval (IR) systems and text classifiers. The task is motivated by recent research showing that the performance of these models drops as the test data becomes more distant in time from the training data. LongEval differs from traditional IR and classification shared task with special considerations on evaluating models that mitigate performance drop over time. We envisage that this task will bring more attention from the NLP community to the problem of temporal generalisability of models, what enables or prevents it, potential solutions and limitations. </p>
<p> The CLEF 2024 LongEval Lab encourages participants to develop temporal information retrieval systems and longitudinal text classifiers that survive through dynamic temporal text changes, introducing time as a new dimension for ranking models performance. </p>
<p> In this page we present CLEF 2025 shared task evaluating the temporal persistence of information retrieval (IR) systems and text classifiers. The task is motivated by recent research showing that the performance of these models drops as the test data becomes more distant in time from the training data. LongEval differs from traditional IR and classification shared task with special considerations on evaluating models that mitigate performance drop over time. We envisage that this task will bring more attention from the NLP community to the problem of temporal generalisability of models, what enables or prevents it, potential solutions and limitations. </p>
<p> The CLEF 2025 LongEval Lab encourages participants to develop temporal information retrieval systems and longitudinal text classifiers that survive through dynamic temporal text changes, introducing time as a new dimension for ranking models performance. </p>

<h3>Previous year</h3>
<p></p>Check our <a href="https://clef-longeval-2023.github.io/">2023 website</a> to find information about previous years.</p>
<p>Check our <a href="https://clef-longeval.github.io/">2024 website</a> and <a href="https://clef-longeval-2023.github.io/">2023 website</a> to find information about previous years.</p>

<h3>Contact</h3>
<h3>Contact XX to be same of change?</h3>
<p><i> For Task 1. LongEval-Retrieval: <a href="mailto: longeval-ir-task@univ-grenoble-alpes.fr">longeval-ir-task@univ-grenoble-alpes.fr</a> </i> </p>
<p><i> For Task 2. LongEval-Classification: <a href="mailto: r.a.a.alkhalifa@qmul.ac.uk">Rabab Alkhalifa</a> </i></p>

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