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<b style="font-family: inherit; font-size: 15px; font-style: inherit; font-variant-ligatures: inherit; font-variant-caps: inherit; color: rgb(36, 36, 36);"><u><span class="x_ContentPasted0" style="font-size: 9pt; font-family: Arial, Helvetica, sans-serif;">Call
for Papers: GenBench, the first workshop on generalisation (benchmarking) in NLP</span></u></b><br>
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<b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Workshop description</span><br class="x_ContentPasted0">
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<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">The ability to generalise well is often mentioned as one of the primary desiderata for models of natural language processing.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">It is crucial to ensure that models behave robustly, reliably and fairly when making predictions about data that is different from the data that they were
trained on.</span><br class="x_ContentPasted0">
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<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Generalisation is also important when NLP models are considered from a cognitive perspective, as models of human language.</span><br class="x_ContentPasted0">
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<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Yet, there are still many open questions related to what it means for an NLP model to generalise well and how generalisation should be evaluated.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">The first GenBench workshop aims to serve as a cornerstone to catalyse research on generalisation in the NLP community.</span>
<div style="margin:0px"><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">In particular, the workshop aims to:</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">-</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Bring
together different expert communities to discuss challenging questions relating to generalisation in NLP;</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">-</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Crowd-source
a collaborative generalisation benchmark</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">hosted
on a platform for democratic state-of-the-art (SOTA) generalisation testing in NLP</span><br class="x_ContentPasted0">
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<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">The first GenBench workshop on generalisation (benchmarking) in NLP will be co-located with EMNLP 2023.</span><br class="x_ContentPasted0">
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<b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Submission types</span><br class="x_ContentPasted0">
</b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">We call for two types of submissions: regular workshop submissions and collaborative benchmarking task submissions.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">The latter will consist of a data/task artefact and a companion paper motivating and evaluating the submission. In both cases, we accept archival papers
and extended abstracts.</span><br class="x_ContentPasted0">
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<i><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">1. Regular workshop submissions</span><br class="x_ContentPasted0">
</i><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Regular workshop submissions present papers on the topic of generalisation (see examples listed below) but are not intended to be included on the GenBench
evaluation platform.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Regular workshop papers may be submitted as an archival paper when they report on completed, original and unpublished research; or as a shorter extended
abstract. More details on this category can be found below.</span><br class="x_ContentPasted0">
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<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Topics of interest include, but are not limited to:</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- Opinion or position papers about generalisation and how it should be evaluated;</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- Analyses of how existing or new models generalise;</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- Empirical studies that propose new paradigms to evaluate generalisation;</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- Meta-analyses that</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">investigate</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> how
results from different generalisation studies compare</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">to
one another</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">;</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- Meta-analyses that study how different types of generalisation are related;</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- Papers that discuss how generalisation of LLMs can be evaluated without access to training data;</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- Papers that discuss why generalisation is (not) important in the era of LLMs</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">;</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- Studies on the relationship between generalisation and fairness or robustness</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">.</span><br class="x_ContentPasted0">
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<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">If you are unsure whether a specific topic is well-suited for submission, feel free to reach out to the organisers of the workshop at</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">genbench@googlegroups.com</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">.</span><br class="x_ContentPasted0">
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<i><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">2. Collaborative Benchmarking Task submissions</span><br class="x_ContentPasted0">
</i><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Collaborative benchmarking task submissions consist of a data/task artefact and a paper describing and motivating the submission and showcasing it on
a select number of models.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">We accept submissions that introduce new datasets, resplits of existing datasets along particular dimensions, or in-context learning tasks</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">,</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">with
the goal of measuring generalisation of NLP models.</span></div>
<div style="margin:0px"><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">We especially encourage submissions that focus on:</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- Generalisation in the context of fairness and inclusivity</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">;</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- Multilingual generalisation</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">;</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- Generalisation in LLMs, where we have no control over the training data</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">.</span><br class="x_ContentPasted0">
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<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Each submission should contain information about the data (URIs, format, preprocessing), model preparation (finetuning loss, ICL prompt templates), and
evaluation metrics. These will be defined either in a configuration file or in code.</span></div>
<div style="margin:0px"><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">More details about the collaborative benchmark </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">task</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">submissions
and example submissions can be found on our website:</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><a href="https://genbench.org/cbt" data-auth="NotApplicable" data-saferedirecturl="https://www.google.com/url?q=https://genbench.org/cbt&source=gmail&ust=1683635236275000&usg=AOvVaw3RJVFXNOxQHy8ebs7Odti3" data-loopstyle="link" data-linkindex="0" class="x_ContentPasted0" style="margin:0px"><span style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">genbench.org/cbt</span></a><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">.</span><br class="x_ContentPasted0">
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<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Participants proposing previously unpublished datasets or splits may choose to submit an archival paper or an extended abstract.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Generalisation evaluation datasets that have already been published elsewhere (or will be published at EMNLP 2023) can be submitted to the platform, as
well, but only through an extended abstract, citing the original publication.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">We allow dual submissions with EMNLP. For more information, see below.</span><br class="x_ContentPasted0">
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<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">If you are in doubt about whether a particular type of dataset is suitable for submission, please consult the information page on our website, or reach
out to the organisers of the workshop at</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">genbench@googlegroups.com</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">.</span><br class="x_ContentPasted0">
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<b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Archival vs extended abstract</span><br class="x_ContentPasted0">
</b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Archival papers are up to 8 pages excluding references and report on completed, original and unpublished research. They follow the requirements of regular
EMNLP 2023 submissions.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Accepted papers will be published in the workshop proceedings and are expected to be presented at the workshop.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">The papers will undergo double-blind peer review and should thus be anonymised.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Extended abstracts can be up to 2 pages excluding references and may report on work in progress or be cross-submissions of work that has already appeared
in another venue.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Abstract titles will be posted on the workshop website but will not be included in the proceedings.</span><br class="x_ContentPasted0">
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<b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Submission instructions</span><br class="x_ContentPasted0">
</b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">For both archival papers and extended abstracts, we refer to the EMNLP 2023 website for paper templates.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Additional requirements for both regular workshop papers and collaborative benchmarking task submissions can be found on our website.</span><br class="x_ContentPasted0">
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<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">All submissions can be submitted through OpenReview; the link can be found on our website.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">We also accept regular workshop submissions (papers of category 1) through the ACL Rolling Review system.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Authors that have their ARR reviews ready may submit their papers and reviews for consideration to the workshop.</span><br class="x_ContentPasted0">
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<b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Important dates</span><br class="x_ContentPasted0">
</b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- August 1, 2023 – Sample data submission deadline</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- September 1, 2023 – Paper submission deadline</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- September 15, 2023 – ARR submission deadline</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- October 6, 2023 – Notification deadline</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- October 18, 2023 – Camera-ready deadline</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">- December 6/7, 2023 – Workshop</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Note: all deadlines are 11:59 PM UTC-12:00</span><br class="x_ContentPasted0">
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<b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Dual submissions</span><br class="x_ContentPasted0">
</b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">We allow dual submissions with EMNLP and encourage relevant papers that were dual-submitted and accepted at EMNLP to redirect to a non-archival extended
abstract submission.</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">We furthermore welcome submissions of extended abstracts that describe work already presented at an earlier venue, both in the collaborative benchmarking </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">task</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">and
in the regular submission track.</span><br class="x_ContentPasted0">
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<b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Preprints</span><br class="x_ContentPasted0">
</b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">We do not have an anonymity deadline. Preprints</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">are
allowed, both before the submission deadline as well as after.</span><br class="x_ContentPasted0">
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<b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Contact</span><br class="x_ContentPasted0">
</b><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Email address:</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">genbench@googlegroups.com</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Website:</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px"> </span><a href="https://genbench.org/workshop" data-auth="NotApplicable" data-saferedirecturl="https://www.google.com/url?q=https://genbench.org/workshop&source=gmail&ust=1683635236275000&usg=AOvVaw3wShG76dmo4n9gv97naK28" data-loopstyle="link" data-linkindex="1" class="x_ContentPasted0" style="margin:0px"><span style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">genbench.org/workshop</span></a></div>
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<i><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">On behalf of the GenBench team,</span><br class="x_ContentPasted0">
</i><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Dieuwke Hupkes</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Khuyagbaatar Batsuren</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Koustuv Sinha</span><br class="x_ContentPasted0">
<span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Amirhossein Kazemnejad</span><br class="x_ContentPasted0">
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<div style="margin:0px"><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Christos Christodoulopoulos</span><br class="x_ContentPasted0">
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<div style="margin:0px"><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Ryan Cotterell</span><br class="x_ContentPasted0">
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<div style="margin:0px"><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Elia Brun</span><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">i</span><br class="x_ContentPasted0">
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<span style="margin:0px"><span class="x_ContentPasted0" style="font-size:9pt; font-family:Arial,Helvetica,sans-serif; margin:0px">Verna Dankers</span></span></div>
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The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336. Is e buidheann carthannais a th’ ann an Oilthigh Dhùn Èideann, clàraichte an Alba, àireamh clàraidh SC005336.
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