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FunctionChat-Bench: Comprehensive Evaluation of Language Models’ Generative Capabilities in Korean Tool-use Dialogs
22 Nov

FunctionChat-Bench: Comprehensive Evaluation of Language Models’ Generative Capabilities in Korean Tool-use Dialogs

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arXiv:2411.14054v1 Announce Type: new Abstract: This study investigates language models' generative capabilities in tool-use dialogs. We categorize the models' outputs in tool-use dialogs into four distinct types: Tool Call, Answer Completion, Slot Question, and Relevance Detection, which serve as aspects for evaluation. We introduce FunctionChat-Bench, comprising 700 evaluation items and automated assessment programs. Using this benchmark, we evaluate several language models that support function calling. Our findings indicate that while language models may exhibit high accuracy in single-turn Tool Call scenarios, this does not necessarily translate to superior generative performance in multi-turn environments. We argue that the capabilities required for…
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Adaptable Embeddings Network (AEN)
22 Nov

Adaptable Embeddings Network (AEN)

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arXiv:2411.13786v1 Announce Type: new Abstract: Modern day Language Models see extensive use in text classification, yet this comes at significant computational cost. Compute-effective classification models are needed for low-resource environments, most notably on edge devices. We introduce Adaptable Embeddings Networks (AEN), a novel dual-encoder architecture using Kernel Density Estimation (KDE). This architecture allows for runtime adaptation of classification criteria without retraining and is non-autoregressive. Through thorough synthetic data experimentation, we demonstrate our model outputs comparable and in certain cases superior results to that of autoregressive models an order of magnitude larger than AEN's size. The architecture's ability to preprocess and…
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Non-Linear Outlier Synthesis for Out-of-Distribution Detection
22 Nov

Non-Linear Outlier Synthesis for Out-of-Distribution Detection

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arXiv:2411.13619v1 Announce Type: new Abstract: The reliability of supervised classifiers is severely hampered by their limitations in dealing with unexpected inputs, leading to great interest in out-of-distribution (OOD) detection. Recently, OOD detectors trained on synthetic outliers, especially those generated by large diffusion models, have shown promising results in defining robust OOD decision boundaries. Building on this progress, we present NCIS, which enhances the quality of synthetic outliers by operating directly in the diffusion's model embedding space rather than combining disjoint models as in previous work and by modeling class-conditional manifolds with a conditional volume-preserving network for more expressive characterization of…
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Verifying the Robustness of Automatic Credibility Assessment
22 Nov

Verifying the Robustness of Automatic Credibility Assessment

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[Submitted on 14 Mar 2023 (v1), last revised 21 Nov 2024 (this version, v3)] View a PDF of the paper titled Verifying the Robustness of Automatic Credibility Assessment, by Piotr Przyby{l}a and 2 other authors View PDF HTML (experimental) Abstract:Text classification methods have been widely investigated as a way to detect content of low credibility: fake news, social media bots, propaganda, etc. Quite accurate models (likely based on deep neural networks) help in moderating public electronic platforms and often cause content creators to face rejection of their submissions or removal of already published texts. Having the incentive to evade further…
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How Big Data Is Transforming Patient Care Delivery
22 Nov

How Big Data Is Transforming Patient Care Delivery

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Advances in technology translate into better, more informed patient care in a number of ways. Utilizing artificial intelligence (AI), health care providers can analyze health risks with various treatment plans and develop a plan of action that is best for each individual. The result is reduced costs and better outcomes.  Here are the ways big data is transforming patient care delivery. 1. Personalized Treatment Plans Researchers describe traditional health care as a trial-and-error approach, where doctors try a medicine and switch to others until they get the desired outcome. The old approach leads to poor outcomes and adverse side effects.  Big…
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HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation
22 Nov

HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation

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[Submitted on 24 Jul 2024 (v1), last revised 21 Nov 2024 (this version, v3)] Authors:Zhenzhi Wang, Yixuan Li, Yanhong Zeng, Youqing Fang, Yuwei Guo, Wenran Liu, Jing Tan, Kai Chen, Tianfan Xue, Bo Dai, Dahua Lin View a PDF of the paper titled HumanVid: Demystifying Training Data for Camera-controllable Human Image Animation, by Zhenzhi Wang and 10 other authors View PDF HTML (experimental) Abstract:Human image animation involves generating videos from a character photo, allowing user control and unlocking the potential for video and movie production. While recent approaches yield impressive results using high-quality training data, the inaccessibility of these datasets…
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Hierarchical Information-sharing Convolutional Neural Network for the Prediction of Arctic Sea Ice Concentration and Velocity
22 Nov

Hierarchical Information-sharing Convolutional Neural Network for the Prediction of Arctic Sea Ice Concentration and Velocity

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[Submitted on 31 Oct 2023 (v1), last revised 20 Nov 2024 (this version, v2)] View a PDF of the paper titled Hierarchical Information-sharing Convolutional Neural Network for the Prediction of Arctic Sea Ice Concentration and Velocity, by Younghyun Koo and 1 other authors View PDF HTML (experimental) Abstract:Forecasting sea ice concentration (SIC) and sea ice velocity (SIV) in the Arctic Ocean is of great significance as the Arctic environment has been changed by the recent warming climate. Given that physical sea ice models require high computational costs with complex parameterization, deep learning techniques can effectively replace the physical model and…
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Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling
22 Nov

Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling

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arXiv:2411.14042v1 Announce Type: new Abstract: Predicting future international events from textual information, such as news articles, has tremendous potential for applications in global policy, strategic decision-making, and geopolitics. However, existing datasets available for this task are often limited in quality, hindering the progress of related research. In this paper, we introduce WORLDREP (WORLD Relationship and Event Prediction), a novel dataset designed to address these limitations by leveraging the advanced reasoning capabilities of large-language models (LLMs). Our dataset features high-quality scoring labels generated through advanced prompt modeling and rigorously validated by domain experts in political science. We showcase the quality and…
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A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles
22 Nov

A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles

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arXiv:2411.13778v1 Announce Type: new Abstract: In autonomous driving, the combination of AI and vehicular technology offers great potential. However, this amalgamation comes with vulnerabilities to adversarial attacks. This survey focuses on the intersection of Adversarial Machine Learning (AML) and autonomous systems, with a specific focus on LiDAR-based systems. We comprehensively explore the threat landscape, encompassing cyber-attacks on sensors and adversarial perturbations. Additionally, we investigate defensive strategies employed in countering these threats. This paper endeavors to present a concise overview of the challenges and advances in securing autonomous driving systems against adversarial threats, emphasizing the need for robust defenses to ensure…
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Video2BEV: Transforming Drone Videos to BEVs for Video-based Geo-localization
22 Nov

Video2BEV: Transforming Drone Videos to BEVs for Video-based Geo-localization

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arXiv:2411.13610v1 Announce Type: new Abstract: Existing approaches to drone visual geo-localization predominantly adopt the image-based setting, where a single drone-view snapshot is matched with images from other platforms. Such task formulation, however, underutilizes the inherent video output of the drone and is sensitive to occlusions and environmental constraints. To address these limitations, we formulate a new video-based drone geo-localization task and propose the Video2BEV paradigm. This paradigm transforms the video into a Bird's Eye View (BEV), simplifying the subsequent matching process. In particular, we employ Gaussian Splatting to reconstruct a 3D scene and obtain the BEV projection. Different from the…
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