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From Text to Insight: Leveraging Large Language Models for Performance Evaluation in Management
13 Aug

From Text to Insight: Leveraging Large Language Models for Performance Evaluation in Management

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arXiv:2408.05328v1 Announce Type: new Abstract: This study explores the potential of Large Language Models (LLMs), specifically GPT-4, to enhance objectivity in organizational task performance evaluations. Through comparative analyses across two studies, including various task performance outputs, we demonstrate that LLMs can serve as a reliable and even superior alternative to human raters in evaluating knowledge-based performance outputs, which are a key contribution of knowledge workers. Our results suggest that GPT ratings are comparable to human ratings but exhibit higher consistency and reliability. Additionally, combined multiple GPT ratings on the same performance output show strong correlations with aggregated human performance ratings,…
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SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks
13 Aug

SocFedGPT: Federated GPT-based Adaptive Content Filtering System Leveraging User Interactions in Social Networks

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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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CAR: Contrast-Agnostic Deformable Medical Image Registration with Contrast-Invariant Latent Regularization
13 Aug

CAR: Contrast-Agnostic Deformable Medical Image Registration with Contrast-Invariant Latent Regularization

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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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A Psychology-based Unified Dynamic Framework for Curriculum Learning
13 Aug

A Psychology-based Unified Dynamic Framework for Curriculum Learning

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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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FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG
13 Aug

FLASH: Federated Learning-Based LLMs for Advanced Query Processing in Social Networks through RAG

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arXiv:2408.05242v1 Announce Type: new Abstract: Our paper introduces a novel approach to social network information retrieval and user engagement through a personalized chatbot system empowered by Federated Learning GPT. The system is designed to seamlessly aggregate and curate diverse social media data sources, including user posts, multimedia content, and trending news. Leveraging Federated Learning techniques, the GPT model is trained on decentralized data sources to ensure privacy and security while providing personalized insights and recommendations. Users interact with the chatbot through an intuitive interface, accessing tailored information and real-time updates on social media trends and user-generated content. The system's innovative…
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VACoDe: Visual Augmented Contrastive Decoding
13 Aug

VACoDe: Visual Augmented Contrastive Decoding

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arXiv:2408.05337v1 Announce Type: new Abstract: Despite the astonishing performance of recent Large Vision-Language Models (LVLMs), these models often generate inaccurate responses. To address this issue, previous studies have focused on mitigating hallucinations by employing contrastive decoding (CD) with augmented images, which amplifies the contrast with the original image. However, these methods have limitations, including reliance on a single augmentation, which is restrictive for certain tasks, as well as the high cost of using external knowledge. In this study, we address these limitations by exploring how to utilize multiple image augmentations. Through extensive experiments, we observed that different augmentations produce varying…
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MUSE: Multi-Knowledge Passing on the Edges, Boosting Knowledge Graph Completion
13 Aug

MUSE: Multi-Knowledge Passing on the Edges, Boosting Knowledge Graph Completion

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arXiv:2408.05283v1 Announce Type: new Abstract: Knowledge Graph Completion (KGC) aims to predict the missing information in the (head entity)-[relation]-(tail entity) triplet. Deep Neural Networks have achieved significant progress in the relation prediction task. However, most existing KGC methods focus on single features (e.g., entity IDs) and sub-graph aggregation, which cannot fully explore all the features in the Knowledge Graph (KG), and neglect the external semantic knowledge injection. To address these problems, we propose MUSE, a knowledge-aware reasoning model to learn a tailored embedding space in three dimensions for missing relation prediction through a multi-knowledge representation learning mechanism. Our MUSE consists…
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Biomimetic Machine Learning approach for prediction of mechanical properties of Additive Friction Stir Deposited Aluminum alloys based walled structures
13 Aug

Biomimetic Machine Learning approach for prediction of mechanical properties of Additive Friction Stir Deposited Aluminum alloys based walled structures

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arXiv:2408.05237v1 Announce Type: new Abstract: This study presents a novel approach to predicting mechanical properties of Additive Friction Stir Deposited (AFSD) aluminum alloy walled structures using biomimetic machine learning. The research combines numerical modeling of the AFSD process with genetic algorithm-optimized machine learning models to predict von Mises stress and logarithmic strain. Finite element analysis was employed to simulate the AFSD process for five aluminum alloys: AA2024, AA5083, AA5086, AA7075, and AA6061, capturing complex thermal and mechanical interactions. A dataset of 200 samples was generated from these simulations. Subsequently, Decision Tree (DT) and Random Forest (RF) regression models, optimized using…
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A Recurrent YOLOv8-based framework for Event-Based Object Detection
13 Aug

A Recurrent YOLOv8-based framework for Event-Based Object Detection

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[Submitted on 9 Aug 2024] View a PDF of the paper titled A Recurrent YOLOv8-based framework for Event-Based Object Detection, by Diego A. Silva and 4 other authors View PDF HTML (experimental) Abstract:Object detection is crucial in various cutting-edge applications, such as autonomous vehicles and advanced robotics systems, primarily relying on data from conventional frame-based RGB sensors. However, these sensors often struggle with issues like motion blur and poor performance in challenging lighting conditions. In response to these challenges, event-based cameras have emerged as an innovative paradigm. These cameras, mimicking the human eye, demonstrate superior performance in environments with fast…
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Large Model Strategic Thinking, Small Model Efficiency: Transferring Theory of Mind in Large Language Models
13 Aug

Large Model Strategic Thinking, Small Model Efficiency: Transferring Theory of Mind in Large Language Models

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arXiv:2408.05241v1 Announce Type: new Abstract: As the performance of larger, newer Large Language Models continues to improve for strategic Theory of Mind (ToM) tasks, the demand for these state of the art models increases commensurately. However, their deployment is costly both in terms of processing power and time. In this paper, we investigate the feasibility of creating smaller, simulation-ready agents by way of fine-tuning. To do this, we present a large pre-trained model with 20 unique scenarios that combine a social context with a social dilemma, recording its answers, and using them for Q&A fine-tuning on a smaller model of…
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