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(metamorworks/Shutterstock) Like ethically sourced diamonds or coffee beans, ethically sourced data can be hard to find. But as AI chews through all the easily sourced training data, the ways and means by which data is obtained are becoming increasingly important. One outfit that’s building a business around ethically sourced data is Prolific. Prolific was founded at Oxford University in 2014 primarily to provide data for academic research. If a behavioral scientist needed data for a study on how consumer decision-making changes with age, for instance, they could tap Prolific to help it find vetted participants and to gather the data…

arXiv:2408.01000v1 Announce Type: new Abstract: The surging demand for cloud computing resources, driven by the rapid growth of sophisticated large-scale models and data centers, underscores the critical importance of efficient and adaptive resource allocation. As major tech enterprises deploy massive infrastructures with thousands of GPUs, existing cloud platforms still struggle with low resource utilization due to key challenges: capturing hierarchical indicator structures, modeling non-Gaussian distributions, and decision-making under uncertainty. To address these challenges, we propose HRAMONY, an adaptive Hierarchical Attention-based Resource Modeling and Decision-Making System. HARMONY combines hierarchical multi-indicator distribution forecasting and uncertainty-aware Bayesian decision-making. It introduces a novel hierarchical…

arXiv:2408.01031v1 Announce Type: new Abstract: Large-scale self-supervised pre-training has paved the way for one foundation model to handle many different vision tasks. Most pre-training methodologies train a single model of a certain size at one time. Nevertheless, various computation or storage constraints in real-world scenarios require substantial efforts to develop a series of models with different sizes to deploy. Thus, in this study, we propose a novel tri-branch self-supervised training framework, termed as POA (Pre-training Once for All), to tackle this aforementioned issue. Our approach introduces an innovative elastic student branch into a modern self-distillation paradigm. At each pre-training step,…

arXiv:2408.01122v1 Announce Type: new Abstract: The adeptness of Large Language Models (LLMs) in comprehending and following natural language instructions is critical for their deployment in sophisticated real-world applications. Existing evaluations mainly focus on fragmented constraints or narrow scenarios, but they overlook the comprehensiveness and authenticity of constraints from the user's perspective. To bridge this gap, we propose CFBench, a large-scale Comprehensive Constraints Following Benchmark for LLMs, featuring 1,000 curated samples that cover more than 200 real-life scenarios and over 50 NLP tasks. CFBench meticulously compiles constraints from real-world instructions and constructs an innovative systematic framework for constraint types, which includes…

Organizations that have placed early bets on the potential of AI could find their expectations unmet, according to Gartner, one of the leading research and advisory firms. Despite the novelty and excitement surrounding GenAI, many organizations are still grappling with how to effectively leverage its potential.  Organizations are looking to deploy GenAI to transform their businesses and create new business opportunities. However, companies are struggling to justify their GenAI investments in terms of productivity enhancements.  The Gartner report predicts that at least 30% of GenAI projects currently in testing will be abandoned after proof of concept by the end of 2025.…

[Submitted on 2 Aug 2024] View a PDF of the paper titled IncidentNet: Traffic Incident Detection, Localization and Severity Estimation with Sparse Sensing, by Sai Shashank Peddiraju and 2 other authors View PDF HTML (experimental) Abstract:Prior art in traffic incident detection relies on high sensor coverage and is primarily based on decision-tree and random forest models that have limited representation capacity and, as a result, cannot detect incidents with high accuracy. This paper presents IncidentNet - a novel approach for classifying, localizing, and estimating the severity of traffic incidents using deep learning models trained on data captured from sparsely placed…

arXiv:2408.01014v1 Announce Type: new Abstract: Text-to-image diffusion models have shown the ability to learn a diverse range of concepts. However, it is worth noting that they may also generate undesirable outputs, consequently giving rise to significant security concerns. Specifically, issues such as Not Safe for Work (NSFW) content and potential violations of style copyright may be encountered. Since image generation is conditioned on text, prompt purification serves as a straightforward solution for content safety. Similar to the approach taken by LLM, some efforts have been made to control the generation of safe outputs by purifying prompts. However, it is also…

arXiv:2408.01119v1 Announce Type: new Abstract: Prompt tuning is a modular and efficient solution for training large language models (LLMs). One of its main advantages is task modularity, making it suitable for multi-task problems. However, current soft-prompt-based methods often sacrifice multi-task modularity, requiring the training process to be fully or partially repeated for each newly added task. While recent work on task vectors applied arithmetic operations on full model weights to achieve the desired multi-task performance, a similar approach for soft-prompts is still missing. To this end, we introduce Task Prompt Vectors, created by element-wise difference between weights of tuned soft-prompts…

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arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Source link lol