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<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/2411.10634">arXiv:2411.10634</a> <span> [<a href="https://arxiv.org/pdf/2411.10634">pdf</a>, <a href="https://arxiv.org/format/2411.10634">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"> Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Helli%2C+K">Kai Helli</a>, <a href="/search/cs?searchtype=author&query=Schnurr%2C+D">David Schnurr</a>, <a href="/search/cs?searchtype=author&query=Hollmann%2C+N">Noah Hollmann</a>, <a href="/search/cs?searchtype=author&query=M%C3%BCller%2C+S">Samuel M眉ller</a>, <a href="/search/cs?searchtype=author&query=Hutter%2C+F">Frank Hutter</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="2411.10634v1-abstract-short" style="display: inline;"> While most ML models expect independent and identically distributed data, this assumption is often violated in real-world scenarios due to distribution shifts, resulting in the degradation of machine learning model performance. Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts. To address temporal distribution shifts, we present Dr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10634v1-abstract-full').style.display = 'inline'; document.getElementById('2411.10634v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10634v1-abstract-full" style="display: none;"> While most ML models expect independent and identically distributed data, this assumption is often violated in real-world scenarios due to distribution shifts, resulting in the degradation of machine learning model performance. Until now, no tabular method has consistently outperformed classical supervised learning, which ignores these shifts. To address temporal distribution shifts, we present Drift-Resilient TabPFN, a fresh approach based on In-Context Learning with a Prior-Data Fitted Network that learns the learning algorithm itself: it accepts the entire training dataset as input and makes predictions on the test set in a single forward pass. Specifically, it learns to approximate Bayesian inference on synthetic datasets drawn from a prior that specifies the model's inductive bias. This prior is based on structural causal models (SCM), which gradually shift over time. To model shifts of these causal models, we use a secondary SCM, that specifies changes in the primary model parameters. The resulting Drift-Resilient TabPFN can be applied to unseen data, runs in seconds on small to moderately sized datasets and needs no hyperparameter tuning. Comprehensive evaluations across 18 synthetic and real-world datasets demonstrate large performance improvements over a wide range of baselines, such as XGB, CatBoost, TabPFN, and applicable methods featured in the Wild-Time benchmark. Compared to the strongest baselines, it improves accuracy from 0.688 to 0.744 and ROC AUC from 0.786 to 0.832 while maintaining stronger calibration. This approach could serve as significant groundwork for further research on out-of-distribution prediction. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10634v1-abstract-full').style.display = 'none'; document.getElementById('2411.10634v1-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> 15 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 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">Accepted at the 38th Conference on Neural Information Processing Systems (NeurIPS 2024)</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T07 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.6 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.07892">arXiv:2408.07892</a> <span> [<a href="https://arxiv.org/pdf/2408.07892">pdf</a>, <a href="https://arxiv.org/format/2408.07892">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> </div> </div> <p class="title is-5 mathjax"> Personhood credentials: Artificial intelligence and the value of privacy-preserving tools to distinguish who is real online </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Adler%2C+S">Steven Adler</a>, <a href="/search/cs?searchtype=author&query=Hitzig%2C+Z">Zo毛 Hitzig</a>, <a href="/search/cs?searchtype=author&query=Jain%2C+S">Shrey Jain</a>, <a href="/search/cs?searchtype=author&query=Brewer%2C+C">Catherine Brewer</a>, <a href="/search/cs?searchtype=author&query=Chang%2C+W">Wayne Chang</a>, <a href="/search/cs?searchtype=author&query=DiResta%2C+R">Ren茅e DiResta</a>, <a href="/search/cs?searchtype=author&query=Lazzarin%2C+E">Eddy Lazzarin</a>, <a href="/search/cs?searchtype=author&query=McGregor%2C+S">Sean McGregor</a>, <a href="/search/cs?searchtype=author&query=Seltzer%2C+W">Wendy Seltzer</a>, <a href="/search/cs?searchtype=author&query=Siddarth%2C+D">Divya Siddarth</a>, <a href="/search/cs?searchtype=author&query=Soliman%2C+N">Nouran Soliman</a>, <a href="/search/cs?searchtype=author&query=South%2C+T">Tobin South</a>, <a href="/search/cs?searchtype=author&query=Spelliscy%2C+C">Connor Spelliscy</a>, <a href="/search/cs?searchtype=author&query=Sporny%2C+M">Manu Sporny</a>, <a href="/search/cs?searchtype=author&query=Srivastava%2C+V">Varya Srivastava</a>, <a href="/search/cs?searchtype=author&query=Bailey%2C+J">John Bailey</a>, <a href="/search/cs?searchtype=author&query=Christian%2C+B">Brian Christian</a>, <a href="/search/cs?searchtype=author&query=Critch%2C+A">Andrew Critch</a>, <a href="/search/cs?searchtype=author&query=Falcon%2C+R">Ronnie Falcon</a>, <a href="/search/cs?searchtype=author&query=Flanagan%2C+H">Heather Flanagan</a>, <a href="/search/cs?searchtype=author&query=Duffy%2C+K+H">Kim Hamilton Duffy</a>, <a href="/search/cs?searchtype=author&query=Ho%2C+E">Eric Ho</a>, <a href="/search/cs?searchtype=author&query=Leibowicz%2C+C+R">Claire R. Leibowicz</a>, <a href="/search/cs?searchtype=author&query=Nadhamuni%2C+S">Srikanth Nadhamuni</a>, <a href="/search/cs?searchtype=author&query=Rozenshtein%2C+A+Z">Alan Z. Rozenshtein</a> , et al. (7 additional authors not shown) </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="2408.07892v4-abstract-short" style="display: inline;"> Anonymity is an important principle online. However, malicious actors have long used misleading identities to conduct fraud, spread disinformation, and carry out other deceptive schemes. With the advent of increasingly capable AI, bad actors can amplify the potential scale and effectiveness of their operations, intensifying the challenge of balancing anonymity and trustworthiness online. In this p… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.07892v4-abstract-full').style.display = 'inline'; document.getElementById('2408.07892v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.07892v4-abstract-full" style="display: none;"> Anonymity is an important principle online. However, malicious actors have long used misleading identities to conduct fraud, spread disinformation, and carry out other deceptive schemes. With the advent of increasingly capable AI, bad actors can amplify the potential scale and effectiveness of their operations, intensifying the challenge of balancing anonymity and trustworthiness online. In this paper, we analyze the value of a new tool to address this challenge: "personhood credentials" (PHCs), digital credentials that empower users to demonstrate that they are real people -- not AIs -- to online services, without disclosing any personal information. Such credentials can be issued by a range of trusted institutions -- governments or otherwise. A PHC system, according to our definition, could be local or global, and does not need to be biometrics-based. Two trends in AI contribute to the urgency of the challenge: AI's increasing indistinguishability from people online (i.e., lifelike content and avatars, agentic activity), and AI's increasing scalability (i.e., cost-effectiveness, accessibility). Drawing on a long history of research into anonymous credentials and "proof-of-personhood" systems, personhood credentials give people a way to signal their trustworthiness on online platforms, and offer service providers new tools for reducing misuse by bad actors. In contrast, existing countermeasures to automated deception -- such as CAPTCHAs -- are inadequate against sophisticated AI, while stringent identity verification solutions are insufficiently private for many use-cases. After surveying the benefits of personhood credentials, we also examine deployment risks and design challenges. We conclude with actionable next steps for policymakers, technologists, and standards bodies to consider in consultation with the public. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.07892v4-abstract-full').style.display = 'none'; document.getElementById('2408.07892v4-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> 17 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 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">63 pages, 7 figures, 5 tables; minor additions to acknowledgments and wording changes for clarity; corrected typo; updated email address reference for author</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2303.08774">arXiv:2303.08774</a> <span> [<a href="https://arxiv.org/pdf/2303.08774">pdf</a>, <a href="https://arxiv.org/format/2303.08774">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> </div> </div> <p class="title is-5 mathjax"> GPT-4 Technical Report </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=OpenAI"> OpenAI</a>, <a href="/search/cs?searchtype=author&query=Achiam%2C+J">Josh Achiam</a>, <a href="/search/cs?searchtype=author&query=Adler%2C+S">Steven Adler</a>, <a href="/search/cs?searchtype=author&query=Agarwal%2C+S">Sandhini Agarwal</a>, <a href="/search/cs?searchtype=author&query=Ahmad%2C+L">Lama Ahmad</a>, <a href="/search/cs?searchtype=author&query=Akkaya%2C+I">Ilge Akkaya</a>, <a href="/search/cs?searchtype=author&query=Aleman%2C+F+L">Florencia Leoni Aleman</a>, <a href="/search/cs?searchtype=author&query=Almeida%2C+D">Diogo Almeida</a>, <a href="/search/cs?searchtype=author&query=Altenschmidt%2C+J">Janko Altenschmidt</a>, <a href="/search/cs?searchtype=author&query=Altman%2C+S">Sam Altman</a>, <a href="/search/cs?searchtype=author&query=Anadkat%2C+S">Shyamal Anadkat</a>, <a href="/search/cs?searchtype=author&query=Avila%2C+R">Red Avila</a>, <a href="/search/cs?searchtype=author&query=Babuschkin%2C+I">Igor Babuschkin</a>, <a href="/search/cs?searchtype=author&query=Balaji%2C+S">Suchir Balaji</a>, <a href="/search/cs?searchtype=author&query=Balcom%2C+V">Valerie Balcom</a>, <a href="/search/cs?searchtype=author&query=Baltescu%2C+P">Paul Baltescu</a>, <a href="/search/cs?searchtype=author&query=Bao%2C+H">Haiming Bao</a>, <a href="/search/cs?searchtype=author&query=Bavarian%2C+M">Mohammad Bavarian</a>, <a href="/search/cs?searchtype=author&query=Belgum%2C+J">Jeff Belgum</a>, <a href="/search/cs?searchtype=author&query=Bello%2C+I">Irwan Bello</a>, <a href="/search/cs?searchtype=author&query=Berdine%2C+J">Jake Berdine</a>, <a href="/search/cs?searchtype=author&query=Bernadett-Shapiro%2C+G">Gabriel Bernadett-Shapiro</a>, <a href="/search/cs?searchtype=author&query=Berner%2C+C">Christopher Berner</a>, <a href="/search/cs?searchtype=author&query=Bogdonoff%2C+L">Lenny Bogdonoff</a>, <a href="/search/cs?searchtype=author&query=Boiko%2C+O">Oleg Boiko</a> , et al. (256 additional authors not shown) </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="2303.08774v6-abstract-short" style="display: inline;"> We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based mo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.08774v6-abstract-full').style.display = 'inline'; document.getElementById('2303.08774v6-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2303.08774v6-abstract-full" style="display: none;"> We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2303.08774v6-abstract-full').style.display = 'none'; document.getElementById('2303.08774v6-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> 4 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 March, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 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">100 pages; updated authors list; fixed author names and added citation</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.10005">arXiv:2201.10005</a> <span> [<a href="https://arxiv.org/pdf/2201.10005">pdf</a>, <a href="https://arxiv.org/format/2201.10005">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"> Text and Code Embeddings by Contrastive Pre-Training </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Neelakantan%2C+A">Arvind Neelakantan</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+T">Tao Xu</a>, <a href="/search/cs?searchtype=author&query=Puri%2C+R">Raul Puri</a>, <a href="/search/cs?searchtype=author&query=Radford%2C+A">Alec Radford</a>, <a href="/search/cs?searchtype=author&query=Han%2C+J+M">Jesse Michael Han</a>, <a href="/search/cs?searchtype=author&query=Tworek%2C+J">Jerry Tworek</a>, <a href="/search/cs?searchtype=author&query=Yuan%2C+Q">Qiming Yuan</a>, <a href="/search/cs?searchtype=author&query=Tezak%2C+N">Nikolas Tezak</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+J+W">Jong Wook Kim</a>, <a href="/search/cs?searchtype=author&query=Hallacy%2C+C">Chris Hallacy</a>, <a href="/search/cs?searchtype=author&query=Heidecke%2C+J">Johannes Heidecke</a>, <a href="/search/cs?searchtype=author&query=Shyam%2C+P">Pranav Shyam</a>, <a href="/search/cs?searchtype=author&query=Power%2C+B">Boris Power</a>, <a href="/search/cs?searchtype=author&query=Nekoul%2C+T+E">Tyna Eloundou Nekoul</a>, <a href="/search/cs?searchtype=author&query=Sastry%2C+G">Girish Sastry</a>, <a href="/search/cs?searchtype=author&query=Krueger%2C+G">Gretchen Krueger</a>, <a href="/search/cs?searchtype=author&query=Schnurr%2C+D">David Schnurr</a>, <a href="/search/cs?searchtype=author&query=Such%2C+F+P">Felipe Petroski Such</a>, <a href="/search/cs?searchtype=author&query=Hsu%2C+K">Kenny Hsu</a>, <a href="/search/cs?searchtype=author&query=Thompson%2C+M">Madeleine Thompson</a>, <a href="/search/cs?searchtype=author&query=Khan%2C+T">Tabarak Khan</a>, <a href="/search/cs?searchtype=author&query=Sherbakov%2C+T">Toki Sherbakov</a>, <a href="/search/cs?searchtype=author&query=Jang%2C+J">Joanne Jang</a>, <a href="/search/cs?searchtype=author&query=Welinder%2C+P">Peter Welinder</a>, <a href="/search/cs?searchtype=author&query=Weng%2C+L">Lilian Weng</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="2201.10005v1-abstract-short" style="display: inline;"> Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, training objective and model architecture. In this work, we show that contrastive pre-training on unsupervised data at scale leads to high quality vector representations of text and code.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.10005v1-abstract-full').style.display = 'inline'; document.getElementById('2201.10005v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.10005v1-abstract-full" style="display: none;"> Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, training objective and model architecture. In this work, we show that contrastive pre-training on unsupervised data at scale leads to high quality vector representations of text and code. The same unsupervised text embeddings that achieve new state-of-the-art results in linear-probe classification also display impressive semantic search capabilities and sometimes even perform competitively with fine-tuned models. On linear-probe classification accuracy averaging over 7 tasks, our best unsupervised model achieves a relative improvement of 4% and 1.8% over previous best unsupervised and supervised text embedding models respectively. The same text embeddings when evaluated on large-scale semantic search attains a relative improvement of 23.4%, 14.7%, and 10.6% over previous best unsupervised methods on MSMARCO, Natural Questions and TriviaQA benchmarks, respectively. Similarly to text embeddings, we train code embedding models on (text, code) pairs, obtaining a 20.8% relative improvement over prior best work on code search. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.10005v1-abstract-full').style.display = 'none'; document.getElementById('2201.10005v1-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> 24 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul 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