id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.18174 | INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with
LLM-based Agent | [
"cs.CE",
"cs.AI",
"q-fin.CP"
] | Recent advancements have underscored the potential of large language model (LLM)-based agents in financial decision-making. Despite this progress, the field currently encounters two main challenges: (1) the lack of a comprehensive LLM agent framework adaptable to a variety of financial tasks, and (2) the absence of sta... | {
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2412.18176 | Molar: Multimodal LLMs with Collaborative Filtering Alignment for
Enhanced Sequential Recommendation | [
"cs.IR",
"cs.AI"
] | Sequential recommendation (SR) systems have evolved significantly over the past decade, transitioning from traditional collaborative filtering to deep learning approaches and, more recently, to large language models (LLMs). While the adoption of LLMs has driven substantial advancements, these models inherently lack col... | {
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2412.18177 | Enhancing Online Continual Learning with Plug-and-Play State Space Model
and Class-Conditional Mixture of Discretization | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Online continual learning (OCL) seeks to learn new tasks from data streams that appear only once, while retaining knowledge of previously learned tasks. Most existing methods rely on replay, focusing on enhancing memory retention through regularization or distillation. However, they often overlook the adaptability of t... | {
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2412.18178 | VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis | [
"cs.CV"
] | Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) are two dominant models for image analysis. While CNNs excel at extracting multi-scale features and ViTs effectively capture global dependencies, both suffer from high computational costs, particularly when processing high-resolution images. Recently, ... | {
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2412.18180 | PCM Selector: Penalized Covariate-Mediator Selection Operator for
Evaluating Linear Causal Effects | [
"stat.ME",
"cs.LG"
] | For a data-generating process for random variables that can be described with a linear structural equation model, we consider a situation in which (i) a set of covariates satisfying the back-door criterion cannot be observed or (ii) such a set can be observed, but standard statistical estimation methods cannot be appli... | {
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2412.18182 | JANUS: A Stablecoin 3.0 Blueprint for Navigating the Stablecoin Trilemma
Through Dual-Token Design, Multi-Collateralization, Soft Peg, and AI-Driven
Stabilization | [
"cs.CE"
] | This paper introduces JANUS, a Stablecoin 3.0 protocol designed to address the stablecoin trilemma--simultaneously improving decentralization (D), capital efficiency (E), and safety-stability (S). Building upon insights from previous stablecoin generations, JANUS leverages a dual-token system (Alpha and Omega), integra... | {
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2412.18184 | Unified Stochastic Framework for Neural Network Quantization and Pruning | [
"cs.LG",
"cs.NA",
"math.NA",
"math.PR"
] | Quantization and pruning are two essential techniques for compressing neural networks, yet they are often treated independently, with limited theoretical analysis connecting them. This paper introduces a unified framework for post-training quantization and pruning using stochastic path-following algorithms. Our approac... | {
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2412.18185 | TextMatch: Enhancing Image-Text Consistency Through Multimodal
Optimization | [
"cs.CV",
"cs.AI"
] | Text-to-image generative models excel in creating images from text but struggle with ensuring alignment and consistency between outputs and prompts. This paper introduces TextMatch, a novel framework that leverages multimodal optimization to address image-text discrepancies in text-to-image (T2I) generation and editing... | {
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2412.18187 | Learning Sign Language Representation using CNN LSTM, 3DCNN, CNN RNN
LSTM and CCN TD | [
"cs.LG"
] | Existing Sign Language Learning applications focus on the demonstration of the sign in the hope that the student will copy a sign correctly. In these cases, only a teacher can confirm that the sign was completed correctly, by reviewing a video captured manually. Sign Language Translation is a widely explored field in v... | {
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2412.18188 | On the Applicability of Zero-Shot Cross-Lingual Transfer Learning for
Sentiment Classification in Distant Language Pairs | [
"cs.CL",
"cs.AI"
] | This research explores the applicability of cross-lingual transfer learning from English to Japanese and Indonesian using the XLM-R pre-trained model. The results are compared with several previous works, either by models using a similar zero-shot approach or a fully-supervised approach, to provide an overview of the z... | {
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2412.18189 | Toward an Automated, Proactive Safety Warning System Development for
Truck Mounted Attenuators in Mobile Work Zones | [
"cs.RO"
] | Even though Truck Mounted Attenuators (TMA)/Autonomous Truck Mounted Attenuators (ATMA) and traffic control devices are increasingly used in mobile work zones to enhance safety, work zone collisions remain a significant safety concern in the United States. In Missouri, there were 63 TMA-related crashes in 2023, a 27% i... | {
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2412.18190 | An Analysis on Automated Metrics for Evaluating Japanese-English Chat
Translation | [
"cs.CL",
"cs.AI"
] | This paper analyses how traditional baseline metrics, such as BLEU and TER, and neural-based methods, such as BERTScore and COMET, score several NMT models performance on chat translation and how these metrics perform when compared to human-annotated scores. The results show that for ranking NMT models in chat translat... | {
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2412.18194 | VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics
Manipulation with Long-Horizon Reasoning Tasks | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.CV"
] | General-purposed embodied agents are designed to understand the users' natural instructions or intentions and act precisely to complete universal tasks. Recently, methods based on foundation models especially Vision-Language-Action models (VLAs) have shown a substantial potential to solve language-conditioned manipulat... | {
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2412.18196 | Robustness-aware Automatic Prompt Optimization | [
"cs.CL",
"cs.LG"
] | The performance of Large Language Models (LLMs) depends on the quality of prompts and the semantic and structural integrity of the input data. However, existing prompt generation methods primarily focus on well-structured input data, often neglecting the impact of perturbed inputs on prompt effectiveness. To address th... | {
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2412.18199 | Leveraging Deep Learning with Multi-Head Attention for Accurate
Extraction of Medicine from Handwritten Prescriptions | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Extracting medication names from handwritten doctor prescriptions is challenging due to the wide variability in handwriting styles and prescription formats. This paper presents a robust method for extracting medicine names using a combination of Mask R-CNN and Transformer-based Optical Character Recognition (TrOCR) wit... | {
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2412.18202 | Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs
Algorithms | [
"cs.LG",
"q-fin.ST"
] | This paper leverages machine learning algorithms to forecast and analyze financial time series. The process begins with a denoising autoencoder to filter out random noise fluctuations from the main contract price data. Then, one-dimensional convolution reduces the dimensionality of the filtered data and extracts key in... | {
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2412.18204 | BoxMAC -- A Boxing Dataset for Multi-label Action Classification | [
"cs.CV"
] | In competitive combat sports like boxing, analyzing a boxers's performance statics is crucial for evaluating the quantity and variety of punches delivered during bouts. These statistics provide valuable data and feedback, which are routinely used for coaching and performance enhancement. We introduce BoxMAC, a real-wor... | {
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2412.18207 | Sharper Error Bounds in Late Fusion Multi-view Clustering Using
Eigenvalue Proportion | [
"cs.LG",
"cs.AI"
] | Multi-view clustering (MVC) aims to integrate complementary information from multiple views to enhance clustering performance. Late Fusion Multi-View Clustering (LFMVC) has shown promise by synthesizing diverse clustering results into a unified consensus. However, current LFMVC methods struggle with noisy and redundant... | {
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2412.18208 | Quantum framework for Reinforcement Learning: integrating Markov
Decision Process, quantum arithmetic, and trajectory search | [
"quant-ph",
"cs.LG"
] | This paper introduces a quantum framework for addressing reinforcement learning (RL) tasks, grounded in the quantum principles and leveraging a fully quantum model of the classical Markov Decision Process (MDP). By employing quantum concepts and a quantum search algorithm, this work presents the implementation and opti... | {
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2412.18212 | Accelerating AIGC Services with Latent Action Diffusion Scheduling in
Edge Networks | [
"cs.LG",
"cs.DC"
] | Artificial Intelligence Generated Content (AIGC) has gained significant popularity for creating diverse content. Current AIGC models primarily focus on content quality within a centralized framework, resulting in a high service delay and negative user experiences. However, not only does the workload of an AIGC task dep... | {
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2412.18214 | SDM-Car: A Dataset for Small and Dim Moving Vehicles Detection in
Satellite Videos | [
"cs.CV"
] | Vehicle detection and tracking in satellite video is essential in remote sensing (RS) applications. However, upon the statistical analysis of existing datasets, we find that the dim vehicles with low radiation intensity and limited contrast against the background are rarely annotated, which leads to the poor effect of ... | {
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2412.18216 | ICM-Assistant: Instruction-tuning Multimodal Large Language Models for
Rule-based Explainable Image Content Moderation | [
"cs.CV",
"cs.CL"
] | Controversial contents largely inundate the Internet, infringing various cultural norms and child protection standards. Traditional Image Content Moderation (ICM) models fall short in producing precise moderation decisions for diverse standards, while recent multimodal large language models (MLLMs), when adopted to gen... | {
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2412.18217 | U-Mamba-Net: A highly efficient Mamba-based U-net style network for
noisy and reverberant speech separation | [
"cs.SD",
"cs.LG",
"eess.AS"
] | The topic of speech separation involves separating mixed speech with multiple overlapping speakers into several streams, with each stream containing speech from only one speaker. Many highly effective models have emerged and proliferated rapidly over time. However, the size and computational load of these models have a... | {
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2412.18218 | On the Effectiveness of Adversarial Training on Malware Classifiers | [
"cs.LG",
"cs.CR"
] | Adversarial Training (AT) has been widely applied to harden learning-based classifiers against adversarial evasive attacks. However, its effectiveness in identifying and strengthening vulnerable areas of the model's decision space while maintaining high performance on clean data of malware classifiers remains an under-... | {
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2412.18219 | Adapter Merging with Centroid Prototype Mapping for Scalable
Class-Incremental Learning | [
"cs.CV"
] | We propose Adapter Merging with Centroid Prototype Mapping (ACMap), an exemplar-free framework for class-incremental learning (CIL) that addresses both catastrophic forgetting and scalability. While existing methods trade-off between inference time and accuracy, ACMap consolidates task-specific adapters into a single a... | {
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2412.18221 | GIMS: Image Matching System Based on Adaptive Graph Construction and
Graph Neural Network | [
"cs.CV",
"cs.LG"
] | Feature-based image matching has extensive applications in computer vision. Keypoints detected in images can be naturally represented as graph structures, and Graph Neural Networks (GNNs) have been shown to outperform traditional deep learning techniques. Consequently, the paradigm of image matching via GNNs has gained... | {
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2412.18222 | Leveraging Convolutional Neural Network-Transformer Synergy for
Predictive Modeling in Risk-Based Applications | [
"q-fin.RM",
"cs.LG"
] | With the development of the financial industry, credit default prediction, as an important task in financial risk management, has received increasing attention. Traditional credit default prediction methods mostly rely on machine learning models, such as decision trees and random forests, but these methods have certain... | {
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2412.18224 | Expand VSR Benchmark for VLLM to Expertize in Spatial Rules | [
"cs.CV",
"cs.AI"
] | Distinguishing spatial relations is a basic part of human cognition which requires fine-grained perception on cross-instance. Although benchmarks like MME, MMBench and SEED comprehensively have evaluated various capabilities which already include visual spatial reasoning(VSR). There is still a lack of sufficient quanti... | {
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2412.18230 | Efficient Detection Framework Adaptation for Edge Computing: A
Plug-and-play Neural Network Toolbox Enabling Edge Deployment | [
"cs.CV"
] | Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios. However, existing edge detection methods face challenges: 1) difficulty balancing detection precision with lightweight models, 2) limited adaptability of generalized deployment designs, and 3) in... | {
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2412.18231 | Towards Macro-AUC oriented Imbalanced Multi-Label Continual Learning | [
"cs.LG"
] | In Continual Learning (CL), while existing work primarily focuses on the multi-class classification task, there has been limited research on Multi-Label Learning (MLL). In practice, MLL datasets are often class-imbalanced, making it inherently challenging, a problem that is even more acute in CL. Due to its sensitivity... | {
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2412.18232 | Efficient Long Context Language Model Retrieval with Compression | [
"cs.IR"
] | Long Context Language Models (LCLMs) have emerged as a new paradigm to perform Information Retrieval (IR), which enables the direct ingestion and retrieval of information by processing an entire corpus in their single context, showcasing the potential to surpass traditional sparse and dense retrieval methods. However, ... | {
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2412.18234 | Conditional Deep Canonical Time Warping | [
"cs.LG"
] | Temporal alignment of sequences is a fundamental challenge in many applications, such as computer vision and bioinformatics, where local time shifting needs to be accounted for. Misalignment can lead to poor model generalization, especially in high-dimensional sequences. Existing methods often struggle with optimizatio... | {
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2412.18235 | Band Prompting Aided SAR and Multi-Spectral Data Fusion Framework for
Local Climate Zone Classification | [
"cs.CV"
] | Local climate zone (LCZ) classification is of great value for understanding the complex interactions between urban development and local climate. Recent studies have increasingly focused on the fusion of synthetic aperture radar (SAR) and multi-spectral data to improve LCZ classification performance. However, it remain... | {
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2412.18237 | Sch\"odinger Bridge Type Diffusion Models as an Extension of Variational
Autoencoders | [
"cs.LG"
] | Generative diffusion models use time-forward and backward stochastic differential equations to connect the data and prior distributions. While conventional diffusion models (e.g., score-based models) only learn the backward process, more flexible frameworks have been proposed to also learn the forward process by employ... | {
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2412.18239 | OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from
Observations | [
"physics.ao-ph",
"cs.LG"
] | In recent years, Artificial Intelligence Weather Prediction (AIWP) models have achieved performance comparable to, or even surpassing, traditional Numerical Weather Prediction (NWP) models by leveraging reanalysis data. However, a less-explored approach involves training AIWP models directly on observational data, enha... | {
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2412.18241 | An Automatic Graph Construction Framework based on Large Language Models
for Recommendation | [
"cs.IR",
"cs.AI"
] | Graph neural networks (GNNs) have emerged as state-of-the-art methods to learn from graph-structured data for recommendation. However, most existing GNN-based recommendation methods focus on the optimization of model structures and learning strategies based on pre-defined graphs, neglecting the importance of the graph ... | {
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2412.18247 | Fr\'echet regression for multi-label feature selection with implicit
regularization | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Fr\'echet regression extends linear regression to model complex responses in metric spaces, making it particularly relevant for multi-label regression, where each instance can have multiple associated labels. However, variable selection within this framework remains underexplored. In this paper, we pro pose a nov... | {
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2412.18248 | Detection and Forecasting of Parkinson Disease Progression from Speech
Signal Features Using MultiLayer Perceptron and LSTM | [
"cs.LG",
"cs.AI",
"cs.SD",
"eess.AS"
] | Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The application of machine learning techniques helps improve the diagnostic accuracy of Parkinson disease detection but only few studies have presented work towards the prediction of disease progression. In this research... | {
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2412.18249 | An Improved Fault Diagnosis Strategy for Induction Motors Using Weighted
Probability Ensemble Deep Learning | [
"eess.SP",
"cs.CV"
] | Early detection of faults in induction motors is crucial for ensuring uninterrupted operations in industrial settings. Among the various fault types encountered in induction motors, bearing, rotor, and stator faults are the most prevalent. This paper introduces a Weighted Probability Ensemble Deep Learning (WPEDL) meth... | {
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2412.18254 | RaCMC: Residual-Aware Compensation Network with Multi-Granularity
Constraints for Fake News Detection | [
"cs.CV"
] | Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist in cross-modal feature fusion and refinement for classification. To address this... | {
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2412.18255 | AdaCo: Overcoming Visual Foundation Model Noise in 3D Semantic
Segmentation via Adaptive Label Correction | [
"cs.CV"
] | Recently, Visual Foundation Models (VFMs) have shown a remarkable generalization performance in 3D perception tasks. However, their effectiveness in large-scale outdoor datasets remains constrained by the scarcity of accurate supervision signals, the extensive noise caused by variable outdoor conditions, and the abunda... | {
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2412.18256 | Robust Semi-Supervised Learning in Open Environments | [
"cs.LG",
"cs.AI"
] | Semi-supervised learning (SSL) aims to improve performance by exploiting unlabeled data when labels are scarce. Conventional SSL studies typically assume close environments where important factors (e.g., label, feature, distribution) between labeled and unlabeled data are consistent. However, more practical tasks invol... | {
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2412.18260 | Investigating Large Language Models for Code Vulnerability Detection: An
Experimental Study | [
"cs.CL"
] | Code vulnerability detection (CVD) is essential for addressing and preventing system security issues, playing a crucial role in ensuring software security. Previous learning-based vulnerability detection methods rely on either fine-tuning medium-size sequence models or training smaller neural networks from scratch. Rec... | {
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2412.18262 | Efficient Contrastive Explanations on Demand | [
"cs.LG"
] | Recent work revealed a tight connection between adversarial robustness and restricted forms of symbolic explanations, namely distance-based (formal) explanations. This connection is significant because it represents a first step towards making the computation of symbolic explanations as efficient as deciding the existe... | {
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2412.18263 | High-Rank Irreducible Cartesian Tensor Decomposition and Bases of
Equivariant Spaces | [
"cs.LG",
"math-ph",
"math.MP",
"physics.chem-ph",
"physics.comp-ph",
"quant-ph"
] | Irreducible Cartesian tensors (ICTs) play a crucial role in the design of equivariant graph neural networks, as well as in theoretical chemistry and chemical physics. Meanwhile, the design space of available linear operations on tensors that preserve symmetry presents a significant challenge. The ICT decomposition and ... | {
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2412.18267 | NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised
Heterogeneous Graph Representation Learning | [
"cs.LG"
] | Real-world graph data environments intrinsically exist noise (e.g., link and structure errors) that inevitably disturb the effectiveness of graph representation and downstream learning tasks. For homogeneous graphs, the latest works use original node features to synthesize a similarity graph that can correct the struct... | {
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2412.18270 | Annotating References to Mythological Entities in French Literature | [
"cs.AI"
] | In this paper, we explore the relevance of large language models (LLMs) for annotating references to Roman and Greek mythological entities in modern and contemporary French literature. We present an annotation scheme and demonstrate that recent LLMs can be directly applied to follow this scheme effectively, although no... | {
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2412.18272 | Simulation-based Approach for Fast Optimal Control of a Stefan Problem
with Application to Cell Therapy | [
"eess.SY",
"cs.SY",
"math.OC"
] | This article describes a new, efficient way of finding control and state trajectories in optimal control problems by transformation into a system of differential-algebraic equations (DAEs). The optimal control and state vectors can be obtained via simulation of the resulting DAE system with the selected DAE solver, eli... | {
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2412.18273 | Sampling Bag of Views for Open-Vocabulary Object Detection | [
"cs.CV",
"cs.AI"
] | Existing open-vocabulary object detection (OVD) develops methods for testing unseen categories by aligning object region embeddings with corresponding VLM features. A recent study leverages the idea that VLMs implicitly learn compositional structures of semantic concepts within the image. Instead of using an individual... | {
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2412.18274 | GenAI Content Detection Task 2: AI vs. Human -- Academic Essay
Authenticity Challenge | [
"cs.CL",
"cs.AI"
] | This paper presents a comprehensive overview of the first edition of the Academic Essay Authenticity Challenge, organized as part of the GenAI Content Detection shared tasks collocated with COLING 2025. This challenge focuses on detecting machine-generated vs. human-authored essays for academic purposes. The task is de... | {
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2412.18276 | UNet--: Memory-Efficient and Feature-Enhanced Network Architecture based
on U-Net with Reduced Skip-Connections | [
"cs.CV",
"eess.IV"
] | U-Net models with encoder, decoder, and skip-connections components have demonstrated effectiveness in a variety of vision tasks. The skip-connections transmit fine-grained information from the encoder to the decoder. It is necessary to maintain the feature maps used by the skip-connections in memory before the decodin... | {
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2412.18277 | Towards Modality Generalization: A Benchmark and Prospective Analysis | [
"cs.CV",
"cs.LG"
] | Multi-modal learning has achieved remarkable success by integrating information from various modalities, achieving superior performance in tasks like recognition and retrieval compared to uni-modal approaches. However, real-world scenarios often present novel modalities that are unseen during training due to resource a... | {
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2412.18279 | Improving Multi-Step Reasoning Abilities of Large Language Models with
Direct Advantage Policy Optimization | [
"cs.AI"
] | The role of reinforcement learning (RL) in enhancing the reasoning of large language models (LLMs) is becoming increasingly significant. Despite the success of RL in many scenarios, there are still many challenges in improving the reasoning of LLMs. One challenge is the sparse reward, which makes optimization difficult... | {
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2412.18281 | GDM4MMIMO: Generative Diffusion Models for Massive MIMO Communications | [
"cs.IT",
"cs.LG",
"eess.SP",
"math.IT"
] | Massive multiple-input multiple-output (MIMO) offers significant advantages in spectral and energy efficiencies, positioning it as a cornerstone technology of fifth-generation (5G) wireless communication systems and a promising solution for the burgeoning data demands anticipated in sixth-generation (6G) networks. In r... | {
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2412.18282 | Improved Feature Generating Framework for Transductive Zero-shot
Learning | [
"cs.CV"
] | Feature Generative Adversarial Networks have emerged as powerful generative models in producing high-quality representations of unseen classes within the scope of Zero-shot Learning (ZSL). This paper delves into the pivotal influence of unseen class priors within the framework of transductive ZSL (TZSL) and illuminates... | {
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2412.18283 | On the Local Complexity of Linear Regions in Deep ReLU Networks | [
"cs.LG"
] | We define the local complexity of a neural network with continuous piecewise linear activations as a measure of the density of linear regions over an input data distribution. We show theoretically that ReLU networks that learn low-dimensional feature representations have a lower local complexity. This allows us to conn... | {
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2412.18287 | Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph
Representation | [
"cs.LG",
"cs.AI",
"cs.SI"
] | Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling co... | {
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2412.18288 | Towards understanding how attention mechanism works in deep learning | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | Attention mechanism has been extensively integrated within mainstream neural network architectures, such as Transformers and graph attention networks. Yet, its underlying working principles remain somewhat elusive. What is its essence? Are there any connections between it and traditional machine learning algorithms? In... | {
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2412.18290 | Dissipation alters modes of information encoding in small quantum
reservoirs near criticality | [
"quant-ph",
"cond-mat.dis-nn",
"cond-mat.stat-mech",
"cs.LG"
] | Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet identifying the mechanisms that underlie enhanced performance remains challenging, particularly in many-body open systems where nonlinear interactions and dissipa... | {
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2412.18291 | DeepCRCEval: Revisiting the Evaluation of Code Review Comment Generation | [
"cs.SE",
"cs.AI",
"cs.CL",
"cs.LG"
] | Code review is a vital but demanding aspect of software development, generating significant interest in automating review comments. Traditional evaluation methods for these comments, primarily based on text similarity, face two major challenges: inconsistent reliability of human-authored comments in open-source project... | {
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2412.18292 | Enhancing Multi-Robot Semantic Navigation Through Multimodal
Chain-of-Thought Score Collaboration | [
"cs.RO"
] | Understanding how humans cooperatively utilize semantic knowledge to explore unfamiliar environments and decide on navigation directions is critical for house service multi-robot systems. Previous methods primarily focused on single-robot centralized planning strategies, which severely limited exploration efficiency. R... | {
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2412.18293 | MineStudio: A Streamlined Package for Minecraft AI Agent Development | [
"cs.AI"
] | Minecraft has emerged as a valuable testbed for embodied intelligence and sequential decision-making research, yet the development and validation of novel agents remains hindered by significant engineering challenges. This paper presents MineStudio, an open-source software package designed to streamline embodied policy... | {
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2412.18294 | An Optimized Path Planning of Manipulator Using Spline Curves and Real
Quantifier Elimination Based on Comprehensive Gr\"obner Systems | [
"cs.RO",
"cs.SC"
] | This paper presents an advanced method for addressing the inverse kinematics and optimal path planning challenges in robot manipulators. The inverse kinematics problem involves determining the joint angles for a given position and orientation of the end-effector. Furthermore, the path planning problem seeks a trajector... | {
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2412.18295 | Pirates of the RAG: Adaptively Attacking LLMs to Leak Knowledge Bases | [
"cs.AI"
] | The growing ubiquity of Retrieval-Augmented Generation (RAG) systems in several real-world services triggers severe concerns about their security. A RAG system improves the generative capabilities of a Large Language Models (LLM) by a retrieval mechanism which operates on a private knowledge base, whose unintended expo... | {
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2412.18296 | Navigating Data Corruption in Machine Learning: Balancing Quality,
Quantity, and Imputation Strategies | [
"cs.LG",
"cs.AI"
] | Data corruption, including missing and noisy data, poses significant challenges in real-world machine learning. This study investigates the effects of data corruption on model performance and explores strategies to mitigate these effects through two experimental setups: supervised learning with NLP tasks (NLP-SL) and d... | {
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2412.18297 | Learning to Play Against Unknown Opponents | [
"cs.GT",
"cs.LG"
] | We consider the problem of a learning agent who has to repeatedly play a general sum game against a strategic opponent who acts to maximize their own payoff by optimally responding against the learner's algorithm. The learning agent knows their own payoff function, but is uncertain about the payoff of their opponent (k... | {
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2412.18298 | Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Video anomaly detection (VAD) has witnessed significant advancements through the integration of large language models (LLMs) and vision-language models (VLMs), addressing critical challenges such as interpretability, temporal reasoning, and generalization in dynamic, open-world scenarios. This paper presents an in-dept... | {
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2412.18299 | M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models | [
"cs.CL",
"cs.AI"
] | With the widespread application of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), enhancing their performance has become a research hotspot. This paper presents a novel multi-prompt ensemble decoding approach designed to bolster the generation quality of LLMs by leveraging the aggregati... | {
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2412.18302 | FameBias: Embedding Manipulation Bias Attack in Text-to-Image Models | [
"cs.CV",
"cs.CR",
"cs.LG"
] | Text-to-Image (T2I) diffusion models have rapidly advanced, enabling the generation of high-quality images that align closely with textual descriptions. However, this progress has also raised concerns about their misuse for propaganda and other malicious activities. Recent studies reveal that attackers can embed biases... | {
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2412.18303 | Efficient and Context-Aware Label Propagation for Zero-/Few-Shot
Training-Free Adaptation of Vision-Language Model | [
"cs.CV"
] | Vision-language models (VLMs) have revolutionized machine learning by leveraging large pre-trained models to tackle various downstream tasks. Despite improvements in label, training, and data efficiency, many state-of-the-art VLMs still require task-specific hyperparameter tuning and fail to fully exploit test samples.... | {
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2412.18315 | Signal Constellation Construction via Radio Frequency Mirrors | [
"cs.IT",
"eess.SP",
"math.IT"
] | By integrating feedback with Radio Frequency (RF) mirrors, we develop a closed-loop media-based modulation system for efficient utilization of the signal space. Specifically, this closed-loop construction optimizes the inherited signal constellation from the media, achieving a significantly larger minimum pairwise Eucl... | {
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2412.18316 | Data-Driven Self-Supervised Graph Representation Learning | [
"cs.LG",
"cs.AI"
] | Self-supervised graph representation learning (SSGRL) is a representation learning paradigm used to reduce or avoid manual labeling. An essential part of SSGRL is graph data augmentation. Existing methods usually rely on heuristics commonly identified through trial and error and are effective only within some applicati... | {
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2412.18319 | Mulberry: Empowering MLLM with o1-like Reasoning and Reflection via
Collective Monte Carlo Tree Search | [
"cs.CV",
"cs.AI"
] | In this work, we aim to develop an MLLM that understands and solves questions by learning to create each intermediate step of the reasoning involved till the final answer. To this end, we propose Collective Monte Carlo Tree Search (CoMCTS), a new learning-to-reason method for MLLMs, which introduces the concept of coll... | {
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2412.18321 | Computer Vision-Driven Gesture Recognition: Toward Natural and Intuitive
Human-Computer | [
"cs.CV"
] | This study mainly explores the application of natural gesture recognition based on computer vision in human-computer interaction, aiming to improve the fluency and naturalness of human-computer interaction through gesture recognition technology. In the fields of virtual reality, augmented reality and smart home, tradit... | {
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2412.18322 | Exploring Graph Mamba: A Comprehensive Survey on State-Space Models for
Graph Learning | [
"cs.LG",
"cs.AI"
] | Graph Mamba, a powerful graph embedding technique, has emerged as a cornerstone in various domains, including bioinformatics, social networks, and recommendation systems. This survey represents the first comprehensive study devoted to Graph Mamba, to address the critical gaps in understanding its applications, challeng... | {
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2412.18327 | HAUR: Human Annotation Understanding and Recognition Through Text-Heavy
Images | [
"cs.CV"
] | Vision Question Answering (VQA) tasks use images to convey critical information to answer text-based questions, which is one of the most common forms of question answering in real-world scenarios. Numerous vision-text models exist today and have performed well on certain VQA tasks. However, these models exhibit signifi... | {
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2412.18328 | On Codes over Eisenstein Integers | [
"cs.IT",
"math.IT"
] | We propose constructions of codes over quotient rings of Eisenstein integers equipped with the Euclidean, square Euclidean, and hexagonal distances as a generalization of codes over Eisenstein integer fields. By set partitioning, we effectively divide the ring of Eisenstein integers into equal-sized subsets for distinc... | {
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2412.18329 | Comprehensive Analysis and Experimental Design of High-Gain DC-DC Boost
Converter Topologies | [
"eess.SY",
"cs.SY"
] | Global demand for clean and eco-friendly energy sources has inspired decades of far-reaching research in power generation from renewable energy sources. Solar cells, wind, and tidal sources are limited in output power generation compared to the fast-rising power requirements of most industrial applications. Besides, th... | {
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2412.18335 | FloNa: Floor Plan Guided Embodied Visual Navigation | [
"cs.RO",
"cs.AI",
"cs.CV"
] | Humans naturally rely on floor plans to navigate in unfamiliar environments, as they are readily available, reliable, and provide rich geometrical guidance. However, existing visual navigation settings overlook this valuable prior knowledge, leading to limited efficiency and accuracy. To eliminate this gap, we introduc... | {
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2412.18337 | The Value of AI-Generated Metadata for UGC Platforms: Evidence from a
Large-scale Field Experiment | [
"econ.GN",
"cs.AI",
"cs.HC",
"q-fin.EC"
] | AI-generated content (AIGC), such as advertisement copy, product descriptions, and social media posts, is becoming ubiquitous in business practices. However, the value of AI-generated metadata, such as titles, remains unclear on user-generated content (UGC) platforms. To address this gap, we conducted a large-scale fie... | {
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2412.18342 | Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in
Open-Set Domain Generalization | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Open-Set Domain Generalization (OSDG) is a challenging task requiring models to accurately predict familiar categories while minimizing confidence for unknown categories to effectively reject them in unseen domains. While the OSDG field has seen considerable advancements, the impact of label noise--a common issue in re... | {
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2412.18344 | Predator Prey Scavenger Model using Holling's Functional Response of
Type III and Physics-Informed Deep Neural Networks | [
"math.DS",
"cs.LG"
] | Nonlinear mathematical models introduce the relation between various physical and biological interactions present in nature. One of the most famous models is the Lotka-Volterra model which defined the interaction between predator and prey species present in nature. However, predators, scavengers, and prey populations c... | {
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2412.18347 | The Constitutional Filter | [
"cs.RO"
] | Predictions in environments where a mix of legal policies, physical limitations, and operational preferences impacts an agent's motion are inherently difficult. Since Neuro-Symbolic systems allow for differentiable information flow between deep learning and symbolic building blocks, they present a promising avenue for ... | {
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2412.18349 | Neural auto-association with optimal Bayesian learning | [
"cs.NE"
] | Neural associative memories are single layer perceptrons with fast synaptic learning typically storing discrete associations between pairs of neural activity patterns. Previous works have analyzed the optimal networks under naive Bayes assumptions of independent pattern components and heteroassociation, where the task ... | {
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2412.18350 | Learning Generalized Residual Exchange-Correlation-Uncertain Functional
for Density Functional Theory | [
"cs.CE"
] | Density Functional Theory (DFT) stands as a widely used and efficient approach for addressing the many-electron Schr\"odinger equation across various domains such as physics, chemistry, and biology. However, a core challenge that persists over the long term pertains to refining the exchange-correlation (XC) approximati... | {
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2412.18351 | Multi-Agents Based on Large Language Models for Knowledge-based Visual
Question Answering | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have achieved impressive results in knowledge-based Visual Question Answering (VQA). However existing methods still have challenges: the inability to use external tools autonomously, and the inability to work in teams. Humans tend to know whether they need to use external tools when they en... | {
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2412.18354 | The Thousand Brains Project: A New Paradigm for Sensorimotor
Intelligence | [
"cs.AI",
"q-bio.NC"
] | Artificial intelligence has advanced rapidly in the last decade, driven primarily by progress in the scale of deep-learning systems. Despite these advances, the creation of intelligent systems that can operate effectively in diverse, real-world environments remains a significant challenge. In this white paper, we outli... | {
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2412.18355 | Addressing Spatial-Temporal Data Heterogeneity in Federated Continual
Learning via Tail Anchor | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Federated continual learning (FCL) allows each client to continually update its knowledge from task streams, enhancing the applicability of federated learning in real-world scenarios. However, FCL needs to address not only spatial data heterogeneity between clients but also temporal data heterogeneity between tasks. In... | {
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2412.18356 | StaR Maps: Unveiling Uncertainty in Geospatial Relations | [
"cs.RO"
] | The growing complexity of intelligent transportation systems and their applications in public spaces has increased the demand for expressive and versatile knowledge representation. While various mapping efforts have achieved widespread coverage, including detailed annotation of features with semantic labels, it is esse... | {
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2412.18357 | SCKF-LSTM Based Trajectory Tracking for Electricity-Gas Integrated
Energy System | [
"eess.SY",
"cs.SY"
] | This paper introduces a novel approach for tracking the dynamic trajectories of integrated natural gas and power systems, leveraging a Kalman filter-based structure. To predict the states of the system, the Holt's exponential smoothing techniques and nonlinear dynamic equations of gas pipelines are applied to establish... | {
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} |
2412.18360 | A universal reproducing kernel Hilbert space for learning nonlinear
systems operators | [
"math.OC",
"cs.SY",
"eess.SY",
"math.DS"
] | In this work, we consider the problem of learning nonlinear operators that correspond to discrete-time nonlinear dynamical systems with inputs. Given an initial state and a finite input trajectory, such operators yield a finite output trajectory compatible with the system dynamics. Inspired by the universal approximati... | {
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} |
2412.18362 | Point-DeepONet: A Deep Operator Network Integrating PointNet for
Nonlinear Analysis of Non-Parametric 3D Geometries and Load Conditions | [
"cs.LG",
"cs.AI"
] | Nonlinear structural analyses in engineering often require extensive finite element simulations, limiting their applicability in design optimization, uncertainty quantification, and real-time control. Conventional deep learning surrogates, such as convolutional neural networks (CNNs), physics-informed neural networks (... | {
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} |
2412.18364 | Extracting triples from dialogues for conversational social agents | [
"cs.CL"
] | Obtaining an explicit understanding of communication within a Hybrid Intelligence collaboration is essential to create controllable and transparent agents. In this paper, we describe a number of Natural Language Understanding models that extract explicit symbolic triples from social conversation. Triple extraction has ... | {
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} |
2412.18365 | Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges | [
"cs.LG",
"cs.AI"
] | Recent studies have shown that Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks. Existing approaches focus on hypergraph modification attacks guided by gradients, overlooking node spanning in the hypergraph and the group identity of hyperedges, thereby resulting in limited attack performance and... | {
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} |
2412.18367 | Towards Global AI Inclusivity: A Large-Scale Multilingual Terminology
Dataset (GIST) | [
"cs.CL"
] | The field of machine translation has achieved significant advancements, yet domain-specific terminology translation, particularly in AI, remains challenging. We introduce GIST, a large-scale multilingual AI terminology dataset containing 5K terms extracted from top AI conference papers spanning 2000 to 2023. The terms ... | {
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} |
2412.18370 | Unveiling the Threat of Fraud Gangs to Graph Neural Networks:
Multi-Target Graph Injection Attacks Against GNN-Based Fraud Detectors | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Graph neural networks (GNNs) have emerged as an effective tool for fraud detection, identifying fraudulent users, and uncovering malicious behaviors. However, attacks against GNN-based fraud detectors and their risks have rarely been studied, thereby leaving potential threats unaddressed. Recent findings suggest that f... | {
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} |
2412.18374 | Non-Minimum-Phase Resonant Controller for Active Damping Control:
Application to Piezo-Actuated Nanopositioning System | [
"eess.SY",
"cs.SY"
] | Nanopositioning systems frequently encounter limitations in control bandwidth due to their lightly damped resonance behavior. This paper presents a novel Non-Minimum-Phase Resonant Controller (NRC) aimed at active damping control within dual closed-loop architectures, specifically applied to piezo-actuated nanoposition... | {
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} |
2412.18375 | A Many Objective Problem Where Crossover is Provably Indispensable | [
"cs.NE",
"cs.AI",
"cs.DS"
] | This paper addresses theory in evolutionary multiobjective optimisation (EMO) and focuses on the role of crossover operators in many-objective optimisation. The advantages of using crossover are hardly understood and rigorous runtime analyses with crossover are lagging far behind its use in practice, specifically in th... | {
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} |
2412.18376 | Bidirectional Topic Matching: Quantifying Thematic Overlap Between
Corpora Through Topic Modelling | [
"cs.CL",
"cs.IR"
] | This study introduces Bidirectional Topic Matching (BTM), a novel method for cross-corpus topic modeling that quantifies thematic overlap and divergence between corpora. BTM is a flexible framework that can incorporate various topic modeling approaches, including BERTopic, Top2Vec, and Latent Dirichlet Allocation (LDA)... | {
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} |
2412.18377 | ChaI-TeA: A Benchmark for Evaluating Autocompletion of Interactions with
LLM-based Chatbots | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The rise of LLMs has deflected a growing portion of human-computer interactions towards LLM-based chatbots. The remarkable abilities of these models allow users to interact using long, diverse natural language text covering a wide range of topics and styles. Phrasing these messages is a time and effort consuming task, ... | {
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} |
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