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<div class="container">
<header>
<h1>Temporal Information Retrieval and Question Answering in the Age of LLMs</h1>
<p class="subtitle">A Comprehensive Tutorial</p>
<p class="conference">WWW 2026 Conference | 13-17 April, 2026 | Dubai, UAE</p>
</header>
<nav>
<ul>
<li><a href="#abstract">Abstract</a></li>
<li><a href="#overview">Overview</a></li>
<li><a href="#schedule">Schedule</a></li>
<li><a href="#materials">Materials</a></li>
<li><a href="#speakers">Speakers</a></li>
<li><a href="#resources">Resources</a></li>
</ul>
</nav>
<section id="abstract">
<h2>Abstract</h2>
<div class="abstract-box">
<p>
Time plays a crucial role in how we retrieve, interpret, and reason over information. As knowledge on the Web continuously evolves, information retrieval (IR) and question answering (QA) systems must recognize not only what is relevant but also when it is valid. This tutorial provides a comprehensive overview of Temporal Information Retrieval (TIR) and Temporal Question Answering (TQA), two closely related fields that address temporal relevance, reasoning, and adaptation in information access.
</p>
<p>
We trace the evolution of temporal methods from early rule-based and probabilistic approaches to modern transformer and large language model (LLM) architectures, highlighting how temporal modeling, reasoning, and retrieval-augmented generation (RAG) are reshaping the field. Participants will learn the fundamental principles of temporal IR/QA, explore pre-LLM and neural methods, and examine recent advances in temporal RAG and temporal reasoning over evolving knowledge.
</p>
<p>
The tutorial concludes with open challenges and future directions for building temporally robust and adaptive AI systems. By bridging classical IR concepts with modern LLM-based reasoning, this tutorial offers a timely and unified perspective on temporal information access for the evolving Web.
</p>
</div>
</section>
<section id="overview">
<h2>Tutorial Overview</h2>
<div class="highlight-box">
<h3>Key Topics Covered</h3>
<ul>
<li>Fundamentals of Temporal Information Retrieval (TIR)</li>
<li>Temporal Question Answering (TQA) systems and architectures</li>
<li>Evolution from rule-based to probabilistic approaches</li>
<li>Modern transformer and LLM architectures for temporal reasoning</li>
<li>Temporal modeling and Retrieval-Augmented Generation (RAG)</li>
<li>Temporal reasoning over evolving knowledge</li>
<li>Recent advances and benchmarks in temporal IR/QA</li>
<li>Open challenges and future research directions</li>
</ul>
</div>
<h3>Target Audience</h3>
<p>
This tutorial is designed for researchers, practitioners, and graduate students interested in information retrieval,
natural language processing, question answering, and large language models. Participants should have basic knowledge
of NLP and machine learning concepts.
</p>
<h3>Learning Outcomes</h3>
<p>After attending this tutorial, participants will be able to:</p>
<ul>
<li>Understand the unique challenges of temporal information retrieval and question answering</li>
<li>Trace the evolution of temporal methods from classical to modern LLM-based approaches</li>
<li>Design and implement temporal QA systems using state-of-the-art techniques</li>
<li>Leverage LLMs and RAG for temporal reasoning tasks</li>
<li>Evaluate temporal IR and QA systems using standard benchmarks</li>
<li>Identify promising research directions and open challenges in temporal information access</li>
</ul>
</section>
<section id="schedule">
<h2>Schedule</h2>
<p><em>Duration: Half-day tutorial</em></p>
<div class="schedule-item">
<h3>1. Introduction and Motivation</h3>
<p><strong>Goal:</strong> Establish why time is a fundamental dimension in information access.</p>
<ul>
<li>Why time matters in IR and QA: recency, evolution, factual decay</li>
<li>Key challenges: knowledge volatility, temporal ambiguity, context shift</li>
<li>Core definitions and taxonomy of temporal IR and QA</li>
</ul>
</div>
<div class="schedule-item">
<h3>2. Core Concepts and Temporal IR Tasks)</h3>
<p><strong>Goal:</strong> Introduce canonical temporal tasks and benchmarks.</p>
<ul>
<li>Temporal intent detection, document dating, focus time estimation</li>
<li>Temporal granularity and temporal query understanding</li>
<li>Benchmark overview: design and evaluation paradigms</li>
</ul>
</div>
<div class="schedule-item">
<h3>3. Foundations: Pre-LLM Temporal IR Models</h3>
<p><strong>Goal:</strong> Review classical methods prior to the neural era.</p>
<ul>
<li>Rule-based, statistical, and temporal language modeling approaches</li>
<li>Frameworks for temporal annotation and normalization</li>
<li>Temporal indexing and ranking strategies</li>
</ul>
</div>
<div class="schedule-item">
<h3>4. Neural and Transformer-based Temporal Models</h3>
<p><strong>Goal:</strong> Explain the evolution of temporal representation learning.</p>
<ul>
<li>Temporal language models</li>
<li>Temporal adaptation, continual learning, and timestamp conditioning</li>
<li>How neural encoders capture or forget temporal signals</li>
</ul>
</div>
<div class="schedule-item">
<h3>Quick Q&A</h3>
<p class="presenter"><strong>All presenters</strong></p>
<p>Clarify foundational concepts and prepare participants for the RAG and reasoning sections.</p>
</div>
<div class="schedule-break">
<h3>☕ Coffee Break</h3>
<p>Informal networking and discussion</p>
</div>
<div class="schedule-item">
<h3>5. Temporal RAG and Reasoning</h3>
<p><strong>Goal:</strong> Explore retrieval and reasoning in LLM-based temporal systems.</p>
<ul>
<li>Integrating temporal retrieval within LLM pipelines (Temporal RAG)</li>
<li>Temporal dense retrievers and alignment</li>
<li>Temporal reasoning: event ordering, duration inference, multi-hop reasoning</li>
<li>Benchmarks for evaluation and temporal reasoning limits of LLMs</li>
</ul>
</div>
<div class="schedule-item">
<h3>6. Temporal Web and Evaluation Ecosystem</h3>
<p><strong>Goal:</strong> Connect temporal IR to the Web context and evaluation resources.</p>
<ul>
<li>Temporal Web analytics and the TempWeb workshop legacy</li>
<li>Evaluating time-aware retrieval on dynamic and archived Web data</li>
<li>Datasets and tools for temporal QA and retrieval</li>
</ul>
</div>
<div class="schedule-item">
<h3>7. Emerging Topics and Open Challenges)</h3>
<p><strong>Goal:</strong> Discuss open challenges and future directions.</p>
<ul>
<li>Temporal uncertainty, diachronic–synchronic integration</li>
<li>Continual updates, temporally aware LLM agents, dynamic evaluation</li>
</ul>
</div>
<div class="schedule-item">
<h3>8. Concluding Discussion and Q&A</h3>
<p><strong>Goal:</strong> Summarize insights and engage participants.</p>
<ul>
<li>Key takeaways and conceptual synthesis</li>
<li>Interactive discussion on open research problems</li>
</ul>
</div>
</section>
<section id="materials">
<h2>Materials</h2>
<p>All tutorial materials will be made available here before and after the tutorial.</p>
<h3>Video Teaser</h3>
<p>
<a href="https://youtu.be/u5Ug8lj85jM" target="_blank" class="btn">Watch Video Teaser</a>
</p>
<h3>Survey Paper</h3>
<p>
<a href="https://arxiv.org/abs/2505.20243" target="_blank" class="btn">It's High Time: A Survey of Temporal Question Answering</a>
</p>
<h3>Slides</h3>
<p>
<p><em>Tutorial slides will be made available here after the tutorial on April 13-17, 2026.</em></p>
</p>
<section id="speakers">
<h2>Tutorial Speakers</h2>
<div class="speaker-grid">
<div class="speaker-card">
<img src="pictures/bhawna-piryani.png" alt="Bhawna Piryani">
<h4><a href="https://bhawnapiryani.github.io/" target="_blank">Bhawna Piryani</a></h4>
<p class="title">Ph.D. Candidate</p>
<p><strong>University of Innsbruck, Austria</strong></p>
</div>
<div class="speaker-card">
<img src="pictures/avishek-anand.webp" alt="Avishek Anand">
<h4><a href="https://www.avishekanand.com/" target="_blank">Avishek Anand</a></h4>
<p class="title">Associate Professor</p>
<p><strong>Delft University of Technology, Netherlands</strong></p>
</div>
<div class="speaker-card">
<img src="pictures/adam-jatowt.jpg" alt="Adam Jatowt">
<h4><a href="https://ds-informatik.uibk.ac.at/doku.php?id=homepage" target="_blank">Adam Jatowt</a></h4>
<p class="title">Professor</p>
<p><strong>University of Innsbruck, Austria</strong></p>
</div>
</div>
</section>
<section id="resources">
<h2>Additional Resources</h2>
<h3>Key Papers</h3>
<p><em>Coming soon - A curated list of key papers in temporal IR and QA will be added here.</em></p>
<h3>Contact</h3>
<p>For questions or inquiries about this tutorial, please contact:</p>
<p>
📧 <a href="mailto:bhawna.piryani@uibk.ac.at">bhawna.piryani@uibk.ac.at</a> (Bhawna Piryani)<br>
📧 <a href="mailto:avishek.anand@tudelft.nl">avishek.anand@tudelft.nl</a> (Avishek Anand)<br>
📧 <a href="mailto:adam.jatowt@uibk.ac.at">adam.jatowt@uibk.ac.at</a> (Adam Jatowt)
</p>
</section>
<footer>
<p>© 2026 WWW Conference Tutorial | Temporal IR & QA in the Age of LLMs</p>
<p>Last updated: January 2025</p>
</footer>
</div>
</body>
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