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Biomedical Event Extraction via Structure-aware Generation
14 Aug

Biomedical Event Extraction via Structure-aware Generation

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arXiv:2408.06583v1 Announce Type: new Abstract: Biomedical Event Extraction (BEE) is a critical task that involves modeling complex relationships between fine-grained entities in biomedical text data. However, most existing BEE models rely on classification methods that neglect the label semantics and argument dependency structure within the data. To address these limitations, we propose GenBEE, a generative model enhanced with a structure-aware prefix for biomedical event extraction. GenBEE constructs event prompts that leverage knowledge distilled from large language models (LLMs), thereby incorporating both label semantics and argument dependency relationships. Additionally, GenBEE introduces a structural prefix learning module that generates structure-aware prefixes with…
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Databricks University Alliance Crosses 1,000 University Threshold
14 Aug

Databricks University Alliance Crosses 1,000 University Threshold

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Databricks is thrilled to share that our University Alliance has welcomed its one-thousandth-member school! This milestone is a testament to our mission to empower universities and colleges around the world with the tools and resources they need to cultivate a new generation of AI talent. With members spanning 85 countries and over 100,000 students, our program is truly global. By equipping faculty with Databricks tools and teaching materials, we are helping students gain the skills and knowledge that will prepare them for real-world careers. Databricks brings AI to your data, and the talented graduates from our member schools are ready…
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Towards Robust and Cost-Efficient Knowledge Unlearning for Large Language Models
14 Aug

Towards Robust and Cost-Efficient Knowledge Unlearning for Large Language Models

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arXiv:2408.06621v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong reasoning and memorization capabilities via pretraining on massive textual corpora. However, training LLMs on human-written text entails significant risk of privacy and copyright violations, which demands an efficient machine unlearning framework to remove knowledge of sensitive data without retraining the model from scratch. While Gradient Ascent (GA) is widely used for unlearning by reducing the likelihood of generating unwanted information, the unboundedness of increasing the cross-entropy loss causes not only unstable optimization, but also catastrophic forgetting of knowledge that needs to be retained. We also discover its joint…
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Prompt Recovery for Image Generation Models: A Comparative Study of Discrete Optimizers
14 Aug

Prompt Recovery for Image Generation Models: A Comparative Study of Discrete Optimizers

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arXiv:2408.06502v1 Announce Type: new Abstract: Recovering natural language prompts for image generation models, solely based on the generated images is a difficult discrete optimization problem. In this work, we present the first head-to-head comparison of recent discrete optimization techniques for the problem of prompt inversion. We evaluate Greedy Coordinate Gradients (GCG), PEZ , Random Search, AutoDAN and BLIP2's image captioner across various evaluation metrics related to the quality of inverted prompts and the quality of the images generated by the inverted prompts. We find that focusing on the CLIP similarity between the inverted prompts and the ground truth image acts…
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OpenEP: Open-Ended Future Event Prediction
14 Aug

OpenEP: Open-Ended Future Event Prediction

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arXiv:2408.06578v1 Announce Type: new Abstract: Future event prediction (FEP) is a long-standing and crucial task in the world, as understanding the evolution of events enables early risk identification, informed decision-making, and strategic planning. Existing work typically treats event prediction as classification tasks and confines the outcomes of future events to a fixed scope, such as yes/no questions, candidate set, and taxonomy, which is difficult to include all possible outcomes of future events. In this paper, we introduce OpenEP (an Open-Ended Future Event Prediction task), which generates flexible and diverse predictions aligned with real-world scenarios. This is mainly reflected in two…
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Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection
14 Aug

Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection

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arXiv:2408.06620v1 Announce Type: new Abstract: Deep learning for time-series anomaly detection (TSAD) has gained significant attention over the past decade. Despite the reported improvements in several papers, the practical application of these models remains limited. Recent studies have cast doubt on these models, attributing their results to flawed evaluation techniques. However, the impact of initialization has largely been overlooked. This paper provides a critical analysis of the initialization effects on TSAD model performance. Our extensive experiments reveal that TSAD models are highly sensitive to hyperparameters such as window size, seed number, and normalization. This sensitivity often leads to significant variability…
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Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples
14 Aug

Generalization Enhancement Strategies to Enable Cross-year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples

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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
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CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarization
14 Aug

CTISum: A New Benchmark Dataset For Cyber Threat Intelligence Summarization

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arXiv:2408.06576v1 Announce Type: new Abstract: Cyber Threat Intelligence (CTI) summarization task requires the system to generate concise and accurate highlights from raw intelligence data, which plays an important role in providing decision-makers with crucial information to quickly detect and respond to cyber threats in the cybersecurity domain. However, efficient techniques for summarizing CTI reports, including facts, analytical insights, attack processes, etc., have largely been unexplored, primarily due to the lack of available dataset. To this end, we present CTISum, a new benchmark for CTI summarization task. Considering the importance of attack process, a novel fine-grained subtask of attack process summarization…
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Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning
14 Aug

Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning

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arXiv:2408.06549v1 Announce Type: new Abstract: Federated Learning (FL) is a distributed machine learning approach that enables devices to collaboratively train models without sharing their local data, ensuring user privacy and scalability. However, applying FL to real-world data presents challenges, particularly as most existing FL research focuses on unimodal data. Multimodal Federated Learning (MFL) has emerged to address these challenges, leveraging modality-specific encoder models to process diverse datasets. Current MFL methods often uniformly allocate computational frequencies across all modalities, which is inefficient for IoT devices with limited resources. In this paper, we propose FlexMod, a novel approach to enhance computational efficiency…
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Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies
14 Aug

Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies

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[Submitted on 12 Aug 2024] View a PDF of the paper titled Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies, by Ahmed Akib Jawad Karim and 2 other authors View PDF HTML (experimental) Abstract:Mosquito-related diseases pose a significant threat to global public health, necessitating efficient and accurate mosquito classification for effective surveillance and control. This work presents an innovative approach to mosquito classification by leveraging state-of-the-art vision transformers and open-set learning techniques. A novel framework has been introduced that integrates Transformer-based deep learning models with comprehensive data augmentation and preprocessing methods, enabling…
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