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<meta name="description" content="FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents">
<meta property="og:title" content="FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents"/>
<meta property="og:description" content="A reusable framework for automatically building information retrieval (IR) evaluation benchmarks from community-asked questions and answers.">
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>
<h1 class="title is-1 publication-title">Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://thakur-nandan.github.io/" target="_blank">Nandan Thakur</a>,<sup>1</sup></span>
<span class="author-block">
<a href="https://cs.uwaterloo.ca/~jimmylin/" target="_blank">Jimmy Lin</a>,<sup>1</sup></span>
<span class="author-block">
<a href="https://www.samuelhavens.com/" target="_blank">Sam Havens</a>,<sup>2</sup></span>
<span class="author-block">
<a href="https://people.csail.mit.edu/mcarbin/" target="_blank">Michael Carbin</a>,<sup>2</sup></span>
<span class="author-block">
<a href="https://omarkhattab.com/" target="_blank">Omar Khattab</a>,<sup>2</sup></span>
<span class="author-block">
<a href="https://mrdrozdov.github.io/" target="_blank">Andrew Drozdov</a><sup>2</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>University of Waterloo <sup>2</sup>Databricks<br>
NeurIPS 2025 Dataset & Benchmark Track<br>
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<p style="margin: 0; font-size: 1.1em;">
<strong style="color: #f57c00;">tldr:</strong> <strong>FreshStack is a holistic framework for building realistic & challenging RAG benchmarks from community-asked questions and answers on niche and fast-growing domains.</strong>
</p>
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Latest News
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style="background-color:#fefefe; border:1px solid #e0e0e0; border-radius:6px; padding:20px; font-size:1.1em; color:#212121;">
<ul style="list-style-type: none; padding-left: 0;">
<li>📊 03/2026: A new preprint on analysing temporal drift in technical documentation in freshstack (LangChain domain): <strong><a href="https://arxiv.org/abs/2603.04532">arxiv.org/abs/2603.04532</a></strong>.</li>
<li>📊 03/2026: <strong><a href="https://www.databricks.com/blog/meet-karl-faster-agent-enterprise-knowledge-powered-custom-rl">KARLBench</a></strong>, a recently released benchmark by Databricks has incorporated FreshStack as one of its datasets.</li>
<li>🎉 11/2025: <strong>FreshStack</strong> received the Honourable Mention for the award of <strong><a href="https://www.bcs.org/membership-and-registrations/member-communities/information-retrieval-specialist-group/awards/bcs-search-industry-awards/">Best Search Project in 2025</a></strong> at the BCS Search Solutions event held in London. </li>
<li>🎯 09/2025: Our roadmap is focused on evaluating new embedding models with extensions to ColBERT-style models soon!</li>
<li>🎉 09/2025: Our paper <strong>FreshStack</strong> has been <strong>accepted at NeurIPS 2025 (Datasets & Benchmarks Track)</strong>. Camera-ready to come soon!</li>
<li>📌 09/2025: <strong>FreshStack</strong> has now been officially included in <strong><a href="https://huggingface.co/blog/rteb">RTEB (Retrieval Embedding Benchmark)</a></strong>, a newer version of the popular MTEB benchmark.</li>
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<p>
We introduce FreshStack, a reusable framework for automatically building information retrieval (IR) evaluation benchmarks from community-asked questions and answers.
FreshStack conducts the following steps: (1) automatic corpus collection from code and technical documentation, (2) nugget generation from community-asked questions and answers, and (3) nugget-level support, retrieving documents using a fusion of retrieval techniques and hybrid architectures.
We use FreshStack to build five datasets on fast-growing, recent, and niche topics to ensure the tasks are sufficiently challenging.
Existing retrieval models, when applied out-of-the-box, significantly underperform oracle approaches on all five topics, denoting plenty of headroom to improve IR quality.
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<h2 class="subtitle has-text-centered">
Video shows a step-by-step procedure followed in FreshStack for constructing evaluation datasets.
</h2>
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</section>
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<div class="container">
<!-------------------------------------------------------------------- LEADERBOARD SECTION -------------------------------------------------------------------->
<div class="columns is-centered m-6">
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<h2 class="title is-3" id="leaderboard">FreshStack Leaderboard</h2>
<div class="content has-text-justified">
<p>
We evaluate various retrieval models on Freshstack (October 2024 version). In each type, we consider both closed- and open-source embedding models.
Our evaluation is conducted under a zero-shot setting to retrieve the relevant document chunks given a query.
We report the results on five datasets: <b>LangChain</b>, <b>Yolo v7 & v8</b>, <b>Laravel 10 & 11</b>, <b>Angular 16, 17 & 18</b>, and <b>Godot4</b>.
If you wish to add your model results in the leaderboard: please submit a PR by adding your model results here: <a href="https://github.com/fresh-stack/fresh-stack.github.io/blob/master/leaderboard_data.json">leaderboard_data.json</a>.
For all models, we choose a maximum sequence length of <b>2048</b> tokens. Queries in Freshstack can be <b>pretty long</b> so make sure to use an embedding model that can handle code and long queries.
</p>
</div>
<br>
<div class="model-labels-container">
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<label class="leaderboard-label upper_baseline">
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Oracle (Stack Overflow Nuggets)
</label>
<label class="leaderboard-label open_source">
<input type="checkbox" class="type-filter" value="open_source" checked>
Open Source
</label>
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Proprietary
</label>
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<br>
<div class="leaderboard-container">
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<table id="freshstack-table">
<thead>
<tr>
<th colspan="4" class="reset-cell" style="text-align: center;">Reset</th>
<th colspan="3" style="text-align: center;" class="average-details-cell">Avg. (5)</th>
<th colspan="3" style="text-align: center;" class="langchain-details-cell">LangChain</th>
<th colspan="3" style="text-align: center;" class="yolo-details-cell">Yolo v7 & v8</th>
<th colspan="3" style="text-align: center;" class="laravel-details-cell">Laravel 10 & 11</th>
<th colspan="3" style="text-align: center;" class="angular-details-cell">Angular 16, 17 & 18</th>
<th colspan="3" style="text-align: center;" class="godot-details-cell">Godot4</th>
</tr>
<tr>
<th>Rank</th>
<th class="sortable clickable" data-sort="string">Retriever</th>
<th class="clickable" data-sort="string">Size</th>
<th class="sortable clickable" data-sort="date">Date</th>
<th class="average-details sortable clickable" data-sort="number">α@10</th>
<th class="average-details sortable clickable" data-sort="number">C@20</th>
<th class="average-details sortable clickable" data-sort="number">R@50</th>
<th class="langchain-details sortable clickable" data-sort="number">α@10</th>
<th class="langchain-details sortable clickable" data-sort="number">C@20</th>
<th class="langchain-details sortable clickable" data-sort="number">R@50</th>
<th class="yolo-details sortable clickable" data-sort="number">α@10</th>
<th class="yolo-details sortable clickable" data-sort="number">C@20</th>
<th class="yolo-details sortable clickable" data-sort="number">R@50</th>
<th class="laravel-details sortable clickable" data-sort="number">α@10</th>
<th class="laravel-details sortable clickable" data-sort="number">C@20</th>
<th class="laravel-details sortable clickable" data-sort="number">R@50</th>
<th class="angular-details sortable clickable" data-sort="number">α@10</th>
<th class="angular-details sortable clickable" data-sort="number">C@20</th>
<th class="angular-details sortable clickable" data-sort="number">R@50</th>
<th class="godot-details sortable clickable" data-sort="number">α@10</th>
<th class="godot-details sortable clickable" data-sort="number">C@20</th>
<th class="godot-details sortable clickable" data-sort="number">R@50</th>
</tr>
</thead>
<tbody>
<!-- Table body will be populated dynamically -->
</tbody>
</table>
<p class="test-desc">
Results of different retrieval models across datasets on Freshstack. The best-performing model in each category is <b>in bold</b>, and the second best is <u>underlined</u>.<br>
<b>α@10</b> (alpha-nDCG@10) measures retrieval diversity & relevance, <b>C@20</b> (Coverage@20) measures fraction of nuggets supported by retrieved documents, <b>R@50</b> (Recall@50) measuring % of relevant documents present in retrieved documents.
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<h2 class="title">BibTeX</h2><pre><code>@inproceedings{
thakur2025freshstack,
title={FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on Technical Documents},
author={Nandan Thakur and Jimmy Lin and Sam Havens and Michael Carbin and Omar Khattab and Andrew Drozdov},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2025},
url={https://openreview.net/forum?id=54TTgXlS2U}
}
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