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name="abstracts" type="radio" value="hide"> <label for="abstracts-1">Hide abstracts</label></li></ul> </div> <div class="box field is-grouped is-grouped-multiline level-item"> <div class="control"> <span class="select is-small"> <select id="size" name="size"><option value="25">25</option><option selected value="50">50</option><option value="100">100</option><option value="200">200</option></select> </span> <label for="size">results per page</label>. </div> <div class="control"> <label for="order">Sort results by</label> <span class="select is-small"> <select id="order" name="order"><option selected value="-announced_date_first">Announcement date (newest first)</option><option value="announced_date_first">Announcement date (oldest first)</option><option value="-submitted_date">Submission date (newest first)</option><option value="submitted_date">Submission date (oldest first)</option><option value="">Relevance</option></select> </span> </div> <div class="control"> <button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.07112">arXiv:2412.07112</a> <span> [<a href="https://arxiv.org/pdf/2412.07112">pdf</a>, <a href="https://arxiv.org/format/2412.07112">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Maya: An Instruction Finetuned Multilingual Multimodal Model </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Alam%2C+N">Nahid Alam</a>, <a href="/search/cs?searchtype=author&query=Kanjula%2C+K+R">Karthik Reddy Kanjula</a>, <a href="/search/cs?searchtype=author&query=Guthikonda%2C+S">Surya Guthikonda</a>, <a href="/search/cs?searchtype=author&query=Chung%2C+T">Timothy Chung</a>, <a href="/search/cs?searchtype=author&query=Vegesna%2C+B+K+S">Bala Krishna S Vegesna</a>, <a href="/search/cs?searchtype=author&query=Das%2C+A">Abhipsha Das</a>, <a href="/search/cs?searchtype=author&query=Susevski%2C+A">Anthony Susevski</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+R+S">Ryan Sze-Yin Chan</a>, <a href="/search/cs?searchtype=author&query=Uddin%2C+S+M+I">S M Iftekhar Uddin</a>, <a href="/search/cs?searchtype=author&query=Islam%2C+S+B">Shayekh Bin Islam</a>, <a href="/search/cs?searchtype=author&query=Santhosh%2C+R">Roshan Santhosh</a>, <a href="/search/cs?searchtype=author&query=A%2C+S">Snegha A</a>, <a href="/search/cs?searchtype=author&query=Sharma%2C+D">Drishti Sharma</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+C">Chen Liu</a>, <a href="/search/cs?searchtype=author&query=Chaturvedi%2C+I">Isha Chaturvedi</a>, <a href="/search/cs?searchtype=author&query=Winata%2C+G+I">Genta Indra Winata</a>, <a href="/search/cs?searchtype=author&query=S%2C+A">Ashvanth. S</a>, <a href="/search/cs?searchtype=author&query=Mukherjee%2C+S">Snehanshu Mukherjee</a>, <a href="/search/cs?searchtype=author&query=Aji%2C+A+F">Alham Fikri Aji</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.07112v1-abstract-short" style="display: inline;"> The rapid development of large Vision-Language Models (VLMs) has led to impressive results on academic benchmarks, primarily in widely spoken languages. However, significant gaps remain in the ability of current VLMs to handle low-resource languages and varied cultural contexts, largely due to a lack of high-quality, diverse, and safety-vetted data. Consequently, these models often struggle to und… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.07112v1-abstract-full').style.display = 'inline'; document.getElementById('2412.07112v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.07112v1-abstract-full" style="display: none;"> The rapid development of large Vision-Language Models (VLMs) has led to impressive results on academic benchmarks, primarily in widely spoken languages. However, significant gaps remain in the ability of current VLMs to handle low-resource languages and varied cultural contexts, largely due to a lack of high-quality, diverse, and safety-vetted data. Consequently, these models often struggle to understand low-resource languages and cultural nuances in a manner free from toxicity. To address these limitations, we introduce Maya, an open-source Multimodal Multilingual model. Our contributions are threefold: 1) a multilingual image-text pretraining dataset in eight languages, based on the LLaVA pretraining dataset; 2) a thorough analysis of toxicity within the LLaVA dataset, followed by the creation of a novel toxicity-free version across eight languages; and 3) a multilingual image-text model supporting these languages, enhancing cultural and linguistic comprehension in vision-language tasks. Code available at https://github.com/nahidalam/maya. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.07112v1-abstract-full').style.display = 'none'; document.getElementById('2412.07112v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 9 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.11847">arXiv:2408.11847</a> <span> [<a href="https://arxiv.org/pdf/2408.11847">pdf</a>, <a href="https://arxiv.org/format/2408.11847">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Prompto: An open source library for asynchronous querying of LLM endpoints </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chan%2C+R+S">Ryan Sze-Yin Chan</a>, <a href="/search/cs?searchtype=author&query=Nanni%2C+F">Federico Nanni</a>, <a href="/search/cs?searchtype=author&query=Williams%2C+A+R">Angus R. Williams</a>, <a href="/search/cs?searchtype=author&query=Brown%2C+E">Edwin Brown</a>, <a href="/search/cs?searchtype=author&query=Burke-Moore%2C+L">Liam Burke-Moore</a>, <a href="/search/cs?searchtype=author&query=Chapman%2C+E">Ed Chapman</a>, <a href="/search/cs?searchtype=author&query=Onslow%2C+K">Kate Onslow</a>, <a href="/search/cs?searchtype=author&query=Sippy%2C+T">Tvesha Sippy</a>, <a href="/search/cs?searchtype=author&query=Bright%2C+J">Jonathan Bright</a>, <a href="/search/cs?searchtype=author&query=Gabasova%2C+E">Evelina Gabasova</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.11847v2-abstract-short" style="display: inline;"> Recent surge in Large Language Model (LLM) availability has opened exciting avenues for research. However, efficiently interacting with these models presents a significant hurdle since LLMs often reside on proprietary or self-hosted API endpoints, each requiring custom code for interaction. Conducting comparative studies between different models can therefore be time-consuming and necessitate sign… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11847v2-abstract-full').style.display = 'inline'; document.getElementById('2408.11847v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.11847v2-abstract-full" style="display: none;"> Recent surge in Large Language Model (LLM) availability has opened exciting avenues for research. However, efficiently interacting with these models presents a significant hurdle since LLMs often reside on proprietary or self-hosted API endpoints, each requiring custom code for interaction. Conducting comparative studies between different models can therefore be time-consuming and necessitate significant engineering effort, hindering research efficiency and reproducibility. To address these challenges, we present prompto, an open source Python library which facilitates asynchronous querying of LLM endpoints enabling researchers to interact with multiple LLMs concurrently, while maximising efficiency and utilising individual rate limits. Our library empowers researchers and developers to interact with LLMs more effectively and allowing faster experimentation, data generation and evaluation. prompto is released with an introductory video (https://youtu.be/lWN9hXBOLyQ) under MIT License and is available via GitHub (https://github.com/alan-turing-institute/prompto). <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11847v2-abstract-full').style.display = 'none'; document.getElementById('2408.11847v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.06731">arXiv:2408.06731</a> <span> [<a href="https://arxiv.org/pdf/2408.06731">pdf</a>, <a href="https://arxiv.org/format/2408.06731">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Large language models can consistently generate high-quality content for election disinformation operations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Williams%2C+A+R">Angus R. Williams</a>, <a href="/search/cs?searchtype=author&query=Burke-Moore%2C+L">Liam Burke-Moore</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+R+S">Ryan Sze-Yin Chan</a>, <a href="/search/cs?searchtype=author&query=Enock%2C+F+E">Florence E. Enock</a>, <a href="/search/cs?searchtype=author&query=Nanni%2C+F">Federico Nanni</a>, <a href="/search/cs?searchtype=author&query=Sippy%2C+T">Tvesha Sippy</a>, <a href="/search/cs?searchtype=author&query=Chung%2C+Y">Yi-Ling Chung</a>, <a href="/search/cs?searchtype=author&query=Gabasova%2C+E">Evelina Gabasova</a>, <a href="/search/cs?searchtype=author&query=Hackenburg%2C+K">Kobi Hackenburg</a>, <a href="/search/cs?searchtype=author&query=Bright%2C+J">Jonathan Bright</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.06731v1-abstract-short" style="display: inline;"> Advances in large language models have raised concerns about their potential use in generating compelling election disinformation at scale. This study presents a two-part investigation into the capabilities of LLMs to automate stages of an election disinformation operation. First, we introduce DisElect, a novel evaluation dataset designed to measure LLM compliance with instructions to generate con… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06731v1-abstract-full').style.display = 'inline'; document.getElementById('2408.06731v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.06731v1-abstract-full" style="display: none;"> Advances in large language models have raised concerns about their potential use in generating compelling election disinformation at scale. This study presents a two-part investigation into the capabilities of LLMs to automate stages of an election disinformation operation. First, we introduce DisElect, a novel evaluation dataset designed to measure LLM compliance with instructions to generate content for an election disinformation operation in localised UK context, containing 2,200 malicious prompts and 50 benign prompts. Using DisElect, we test 13 LLMs and find that most models broadly comply with these requests; we also find that the few models which refuse malicious prompts also refuse benign election-related prompts, and are more likely to refuse to generate content from a right-wing perspective. Secondly, we conduct a series of experiments (N=2,340) to assess the "humanness" of LLMs: the extent to which disinformation operation content generated by an LLM is able to pass as human-written. Our experiments suggest that almost all LLMs tested released since 2022 produce election disinformation operation content indiscernible by human evaluators over 50% of the time. Notably, we observe that multiple models achieve above-human levels of humanness. Taken together, these findings suggest that current LLMs can be used to generate high-quality content for election disinformation operations, even in hyperlocalised scenarios, at far lower costs than traditional methods, and offer researchers and policymakers an empirical benchmark for the measurement and evaluation of these capabilities in current and future models. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06731v1-abstract-full').style.display = 'none'; document.getElementById('2408.06731v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.02329">arXiv:2406.02329</a> <span> [<a href="https://arxiv.org/pdf/2406.02329">pdf</a>, <a href="https://arxiv.org/format/2406.02329">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> On Affine Homotopy between Language Encoders </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chan%2C+R+S">Robin SM Chan</a>, <a href="/search/cs?searchtype=author&query=Boumasmoud%2C+R">Reda Boumasmoud</a>, <a href="/search/cs?searchtype=author&query=Svete%2C+A">Anej Svete</a>, <a href="/search/cs?searchtype=author&query=Ren%2C+Y">Yuxin Ren</a>, <a href="/search/cs?searchtype=author&query=Guo%2C+Q">Qipeng Guo</a>, <a href="/search/cs?searchtype=author&query=Jin%2C+Z">Zhijing Jin</a>, <a href="/search/cs?searchtype=author&query=Ravfogel%2C+S">Shauli Ravfogel</a>, <a href="/search/cs?searchtype=author&query=Sachan%2C+M">Mrinmaya Sachan</a>, <a href="/search/cs?searchtype=author&query=Sch%C3%B6lkopf%2C+B">Bernhard Sch枚lkopf</a>, <a href="/search/cs?searchtype=author&query=El-Assady%2C+M">Mennatallah El-Assady</a>, <a href="/search/cs?searchtype=author&query=Cotterell%2C+R">Ryan Cotterell</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.02329v2-abstract-short" style="display: inline;"> Pre-trained language encoders -- functions that represent text as vectors -- are an integral component of many NLP tasks. We tackle a natural question in language encoder analysis: What does it mean for two encoders to be similar? We contend that a faithful measure of similarity needs to be \emph{intrinsic}, that is, task-independent, yet still be informative of \emph{extrinsic} similarity -- the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02329v2-abstract-full').style.display = 'inline'; document.getElementById('2406.02329v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.02329v2-abstract-full" style="display: none;"> Pre-trained language encoders -- functions that represent text as vectors -- are an integral component of many NLP tasks. We tackle a natural question in language encoder analysis: What does it mean for two encoders to be similar? We contend that a faithful measure of similarity needs to be \emph{intrinsic}, that is, task-independent, yet still be informative of \emph{extrinsic} similarity -- the performance on downstream tasks. It is common to consider two encoders similar if they are \emph{homotopic}, i.e., if they can be aligned through some transformation. In this spirit, we study the properties of \emph{affine} alignment of language encoders and its implications on extrinsic similarity. We find that while affine alignment is fundamentally an asymmetric notion of similarity, it is still informative of extrinsic similarity. We confirm this on datasets of natural language representations. Beyond providing useful bounds on extrinsic similarity, affine intrinsic similarity also allows us to begin uncovering the structure of the space of pre-trained encoders by defining an order over them. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.02329v2-abstract-full').style.display = 'none'; document.getElementById('2406.02329v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 4 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, Accepted at NeurIPS 2024 (Main)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.00708">arXiv:2405.00708</a> <span> [<a href="https://arxiv.org/pdf/2405.00708">pdf</a>, <a href="https://arxiv.org/format/2405.00708">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Interactive Analysis of LLMs using Meaningful Counterfactuals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Cheng%2C+F">Furui Cheng</a>, <a href="/search/cs?searchtype=author&query=Zouhar%2C+V">Vil茅m Zouhar</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+R+S+M">Robin Shing Moon Chan</a>, <a href="/search/cs?searchtype=author&query=F%C3%BCrst%2C+D">Daniel F眉rst</a>, <a href="/search/cs?searchtype=author&query=Strobelt%2C+H">Hendrik Strobelt</a>, <a href="/search/cs?searchtype=author&query=El-Assady%2C+M">Mennatallah El-Assady</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.00708v1-abstract-short" style="display: inline;"> Counterfactual examples are useful for exploring the decision boundaries of machine learning models and determining feature attributions. How can we apply counterfactual-based methods to analyze and explain LLMs? We identify the following key challenges. First, the generated textual counterfactuals should be meaningful and readable to users and thus can be mentally compared to draw conclusions. Se… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.00708v1-abstract-full').style.display = 'inline'; document.getElementById('2405.00708v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.00708v1-abstract-full" style="display: none;"> Counterfactual examples are useful for exploring the decision boundaries of machine learning models and determining feature attributions. How can we apply counterfactual-based methods to analyze and explain LLMs? We identify the following key challenges. First, the generated textual counterfactuals should be meaningful and readable to users and thus can be mentally compared to draw conclusions. Second, to make the solution scalable to long-form text, users should be equipped with tools to create batches of counterfactuals from perturbations at various granularity levels and interactively analyze the results. In this paper, we tackle the above challenges and contribute 1) a novel algorithm for generating batches of complete and meaningful textual counterfactuals by removing and replacing text segments in different granularities, and 2) LLM Analyzer, an interactive visualization tool to help users understand an LLM's behaviors by interactively inspecting and aggregating meaningful counterfactuals. We evaluate the proposed algorithm by the grammatical correctness of its generated counterfactuals using 1,000 samples from medical, legal, finance, education, and news datasets. In our experiments, 97.2% of the counterfactuals are grammatically correct. Through a use case, user studies, and feedback from experts, we demonstrate the usefulness and usability of the proposed interactive visualization tool. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.00708v1-abstract-full').style.display = 'none'; document.getElementById('2405.00708v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7; H.5.2 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.15814">arXiv:2402.15814</a> <span> [<a href="https://arxiv.org/pdf/2402.15814">pdf</a>, <a href="https://arxiv.org/format/2402.15814">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computational Complexity">cs.CC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> On Efficiently Representing Regular Languages as RNNs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Svete%2C+A">Anej Svete</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+R+S+M">Robin Shing Moon Chan</a>, <a href="/search/cs?searchtype=author&query=Cotterell%2C+R">Ryan Cotterell</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.15814v2-abstract-short" style="display: inline;"> Recent work by Hewitt et al. (2020) provides an interpretation of the empirical success of recurrent neural networks (RNNs) as language models (LMs). It shows that RNNs can efficiently represent bounded hierarchical structures that are prevalent in human language. This suggests that RNNs' success might be linked to their ability to model hierarchy. However, a closer inspection of Hewitt et al.'s (… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.15814v2-abstract-full').style.display = 'inline'; document.getElementById('2402.15814v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.15814v2-abstract-full" style="display: none;"> Recent work by Hewitt et al. (2020) provides an interpretation of the empirical success of recurrent neural networks (RNNs) as language models (LMs). It shows that RNNs can efficiently represent bounded hierarchical structures that are prevalent in human language. This suggests that RNNs' success might be linked to their ability to model hierarchy. However, a closer inspection of Hewitt et al.'s (2020) construction shows that it is not inherently limited to hierarchical structures. This poses a natural question: What other classes of LMs can RNNs efficiently represent? To this end, we generalize Hewitt et al.'s (2020) construction and show that RNNs can efficiently represent a larger class of LMs than previously claimed -- specifically, those that can be represented by a pushdown automaton with a bounded stack and a specific stack update function. Altogether, the efficiency of representing this diverse class of LMs with RNN LMs suggests novel interpretations of their inductive bias. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.15814v2-abstract-full').style.display = 'none'; document.getElementById('2402.15814v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.03523">arXiv:2312.03523</a> <span> [<a href="https://arxiv.org/pdf/2312.03523">pdf</a>, <a href="https://arxiv.org/format/2312.03523">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Sig-Networks Toolkit: Signature Networks for Longitudinal Language Modelling </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Tseriotou%2C+T">Talia Tseriotou</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+R+S">Ryan Sze-Yin Chan</a>, <a href="/search/cs?searchtype=author&query=Tsakalidis%2C+A">Adam Tsakalidis</a>, <a href="/search/cs?searchtype=author&query=Bilal%2C+I+M">Iman Munire Bilal</a>, <a href="/search/cs?searchtype=author&query=Kochkina%2C+E">Elena Kochkina</a>, <a href="/search/cs?searchtype=author&query=Lyons%2C+T">Terry Lyons</a>, <a href="/search/cs?searchtype=author&query=Liakata%2C+M">Maria Liakata</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.03523v2-abstract-short" style="display: inline;"> We present an open-source, pip installable toolkit, Sig-Networks, the first of its kind for longitudinal language modelling. A central focus is the incorporation of Signature-based Neural Network models, which have recently shown success in temporal tasks. We apply and extend published research providing a full suite of signature-based models. Their components can be used as PyTorch building block… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03523v2-abstract-full').style.display = 'inline'; document.getElementById('2312.03523v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.03523v2-abstract-full" style="display: none;"> We present an open-source, pip installable toolkit, Sig-Networks, the first of its kind for longitudinal language modelling. A central focus is the incorporation of Signature-based Neural Network models, which have recently shown success in temporal tasks. We apply and extend published research providing a full suite of signature-based models. Their components can be used as PyTorch building blocks in future architectures. Sig-Networks enables task-agnostic dataset plug-in, seamless pre-processing for sequential data, parameter flexibility, automated tuning across a range of models. We examine signature networks under three different NLP tasks of varying temporal granularity: counselling conversations, rumour stance switch and mood changes in social media threads, showing SOTA performance in all three, and provide guidance for future tasks. We release the Toolkit as a PyTorch package with an introductory video, Git repositories for preprocessing and modelling including sample notebooks on the modeled NLP tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.03523v2-abstract-full').style.display = 'none'; document.getElementById('2312.03523v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">To appear in EACL 2024: System Demonstrations</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2309.09770">arXiv:2309.09770</a> <span> [<a href="https://arxiv.org/pdf/2309.09770">pdf</a>, <a href="https://arxiv.org/format/2309.09770">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> How to Data in Datathons </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mougan%2C+C">Carlos Mougan</a>, <a href="/search/cs?searchtype=author&query=Plant%2C+R">Richard Plant</a>, <a href="/search/cs?searchtype=author&query=Teng%2C+C">Clare Teng</a>, <a href="/search/cs?searchtype=author&query=Bazzi%2C+M">Marya Bazzi</a>, <a href="/search/cs?searchtype=author&query=Cabrejas-Egea%2C+A">Alvaro Cabrejas-Egea</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+R+S">Ryan Sze-Yin Chan</a>, <a href="/search/cs?searchtype=author&query=Jasin%2C+D+S">David Salvador Jasin</a>, <a href="/search/cs?searchtype=author&query=Stoffel%2C+M">Martin Stoffel</a>, <a href="/search/cs?searchtype=author&query=Whitaker%2C+K+J">Kirstie Jane Whitaker</a>, <a href="/search/cs?searchtype=author&query=Manser%2C+J">Jules Manser</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2309.09770v4-abstract-short" style="display: inline;"> The rise of datathons, also known as data or data science hackathons, has provided a platform to collaborate, learn, and innovate in a short timeframe. Despite their significant potential benefits, organizations often struggle to effectively work with data due to a lack of clear guidelines and best practices for potential issues that might arise. Drawing on our own experiences and insights from or… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.09770v4-abstract-full').style.display = 'inline'; document.getElementById('2309.09770v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2309.09770v4-abstract-full" style="display: none;"> The rise of datathons, also known as data or data science hackathons, has provided a platform to collaborate, learn, and innovate in a short timeframe. Despite their significant potential benefits, organizations often struggle to effectively work with data due to a lack of clear guidelines and best practices for potential issues that might arise. Drawing on our own experiences and insights from organizing >80 datathon challenges with >60 partnership organizations since 2016, we provide guidelines and recommendations that serve as a resource for organizers to navigate the data-related complexities of datathons. We apply our proposed framework to 10 case studies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2309.09770v4-abstract-full').style.display = 'none'; document.getElementById('2309.09770v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmark</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.03487">arXiv:2006.03487</a> <span> [<a href="https://arxiv.org/pdf/2006.03487">pdf</a>, <a href="https://arxiv.org/format/2006.03487">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path Signature </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Shao%2C+Z">Zhen Shao</a>, <a href="/search/cs?searchtype=author&query=Chan%2C+R+S">Ryan Sze-Yin Chan</a>, <a href="/search/cs?searchtype=author&query=Cochrane%2C+T">Thomas Cochrane</a>, <a href="/search/cs?searchtype=author&query=Foster%2C+P">Peter Foster</a>, <a href="/search/cs?searchtype=author&query=Lyons%2C+T">Terry Lyons</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2006.03487v2-abstract-short" style="display: inline;"> In this paper, we propose a dimensionless anomaly detection method for multivariate streams. Our method is independent of the unit of measurement for the different stream channels, therefore dimensionless. We first propose the variance norm, a generalisation of Mahalanobis distance to handle infinite-dimensional feature space and singular empirical covariance matrix rigorously. We then combine the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.03487v2-abstract-full').style.display = 'inline'; document.getElementById('2006.03487v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.03487v2-abstract-full" style="display: none;"> In this paper, we propose a dimensionless anomaly detection method for multivariate streams. Our method is independent of the unit of measurement for the different stream channels, therefore dimensionless. We first propose the variance norm, a generalisation of Mahalanobis distance to handle infinite-dimensional feature space and singular empirical covariance matrix rigorously. We then combine the variance norm with the path signature, an infinite collection of iterated integrals that provide global features of streams, to propose SigMahaKNN, a method for anomaly detection on (multivariate) streams. We show that SigMahaKNN is invariant to stream reparametrisation, stream concatenation and has a graded discrimination power depending on the truncation level of the path signature. We implement SigMahaKNN as an open-source software, and perform extensive numerical experiments, showing significantly improved anomaly detection on streams compared to isolation forest and local outlier factors in applications ranging from language analysis, hand-writing analysis, ship movement paths analysis and univariate time-series analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.03487v2-abstract-full').style.display = 'none'; document.getElementById('2006.03487v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 6 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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