id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.05945 | NeKo: Toward Post Recognition Generative Correction Large Language
Models with Task-Oriented Experts | [
"cs.CL",
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"cs.LG",
"cs.MA",
"eess.AS"
] | Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The answer would lie in learning dataset-specific features and digesting their knowledge in a single model. Previous methods achieve this by havin... | {
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2411.05946 | Querying Perception Streams with Spatial Regular Expressions | [
"cs.RO",
"cs.CV",
"cs.FL"
] | Perception in fields like robotics, manufacturing, and data analysis generates large volumes of temporal and spatial data to effectively capture their environments. However, sorting through this data for specific scenarios is a meticulous and error-prone process, often dependent on the application, and lacks generality... | {
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2411.05951 | Approaching multifractal complexity in decentralized cryptocurrency
trading | [
"q-fin.ST",
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"q-fin.TR",
"stat.AP"
] | Multifractality is a concept that helps compactly grasping the most essential features of the financial dynamics. In its fully developed form, this concept applies to essentially all mature financial markets and even to more liquid cryptocurrencies traded on the centralized exchanges. A new element that adds complexity... | {
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2411.05952 | Tackling extreme urban heat: a machine learning approach to assess the
impacts of climate change and the efficacy of climate adaptation strategies
in urban microclimates | [
"physics.ao-ph",
"cs.LG"
] | As urbanization and climate change progress, urban heat becomes a priority for climate adaptation efforts. High temperatures concentrated in urban heat can drive increased risk of heat-related death and illness as well as increased energy demand for cooling. However, estimating the effects of urban heat is an ongoing f... | {
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2411.05958 | Sentiment Analysis of Cyberbullying Data in Social Media | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | Social media has become an integral part of modern life, but it has also brought with it the pervasive issue of cyberbullying a serious menace in today's digital age. Cyberbullying, a form of harassment that occurs on social networks, has escalated alongside the growth of these platforms. Sentiment analysis holds signi... | {
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2411.05959 | Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts
for Colonic Pathology Diagnostics | [
"eess.IV",
"cs.CV"
] | Colorectal cancer (CRC) is one of the few cancers that have an established dysplasia-carcinoma sequence that benefits from screening. Everyone over 50 years of age in Canada is eligible for CRC screening. About 20\% of those people will undergo a biopsy for a pre-neoplastic polyp and, in many cases, multiple polyps. As... | {
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2411.05960 | A method based on Generative Adversarial Networks for disentangling
physical and chemical properties of stars in astronomical spectra | [
"astro-ph.IM",
"astro-ph.SR",
"cs.LG"
] | Data compression techniques focused on information preservation have become essential in the modern era of big data. In this work, an encoder-decoder architecture has been designed, where adversarial training, a modification of the traditional autoencoder, is used in the context of astrophysical spectral analysis. The ... | {
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2411.05961 | Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language
Models | [
"cs.CV",
"cs.AI"
] | Vision Language Models (VLMs) are central to Visual Question Answering (VQA) systems and are typically deployed in the cloud due to their high computational demands. However, this cloud-only approach underutilizes edge computational resources and requires significant bandwidth for transmitting raw images. In this paper... | {
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2411.05963 | Assessing Foundational Medical 'Segment Anything' (Med-SAM1, Med-SAM2)
Deep Learning Models for Left Atrial Segmentation in 3D LGE MRI | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with heart failure and stroke. Accurate segmentation of the left atrium (LA) in 3D late gadolinium-enhanced (LGE) MRI is helpful for evaluating AF, as fibrotic remodeling in the LA myocardium contributes to arrhythmia and serves as a key determ... | {
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2411.05964 | Utilisation of Vision Systems and Digital Twin for Maintaining
Cleanliness in Public Spaces | [
"cs.CV",
"eess.IV"
] | Nowadays, the increasing demand for maintaining high cleanliness standards in public spaces results in the search for innovative solutions. The deployment of CCTV systems equipped with modern cameras and software enables not only real-time monitoring of the cleanliness status but also automatic detection of impurities ... | {
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2411.05966 | Energy Efficient Protein Language Models: Leveraging Small Language
Models with LoRA for Controllable Protein Generation | [
"q-bio.BM",
"cs.LG"
] | Large language models (LLMs) have demonstrated significant success in natural language processing (NLP) tasks and have shown promising results in other domains such as protein sequence generation. However, there remain salient differences between LLMs used for NLP, which effectively handle multiple tasks and are availa... | {
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2411.05969 | Toward Transdisciplinary Approaches to Audio Deepfake Discernment | [
"cs.SD",
"cs.CL",
"eess.AS"
] | This perspective calls for scholars across disciplines to address the challenge of audio deepfake detection and discernment through an interdisciplinary lens across Artificial Intelligence methods and linguistics. With an avalanche of tools for the generation of realistic-sounding fake speech on one side, the detection... | {
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2411.05975 | Adaptive Tracking Control with Binary-Valued Output Observations | [
"eess.SY",
"cs.SY"
] | This paper considers real-time control and learning problems for finite-dimensional linear systems under binary-valued and randomly disturbed output observations. This has long been regarded as an open problem because the exact values of the traditional regression vectors used in the construction of adaptive algorithms... | {
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2411.05978 | The Empirical Impact of Data Sanitization on Language Models | [
"cs.CL"
] | Data sanitization in the context of language modeling involves identifying sensitive content, such as personally identifiable information (PII), and redacting them from a dataset corpus. It is a common practice used in natural language processing (NLP) to maintain privacy. Nevertheless, the impact of data sanitization ... | {
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2411.05979 | Variance-Aware Linear UCB with Deep Representation for Neural Contextual
Bandits | [
"cs.LG",
"stat.ML"
] | By leveraging the representation power of deep neural networks, neural upper confidence bound (UCB) algorithms have shown success in contextual bandits. To further balance the exploration and exploitation, we propose Neural-$\sigma^2$-LinearUCB, a variance-aware algorithm that utilizes $\sigma^2_t$, i.e., an upper boun... | {
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2411.05980 | FactLens: Benchmarking Fine-Grained Fact Verification | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Large Language Models (LLMs) have shown impressive capability in language generation and understanding, but their tendency to hallucinate and produce factually incorrect information remains a key limitation. To verify LLM-generated contents and claims from other sources, traditional verification approaches often rely o... | {
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2411.05982 | Unmasking the Shadows: Pinpoint the Implementations of Anti-Dynamic
Analysis Techniques in Malware Using LLM | [
"cs.CR",
"cs.AI"
] | Sandboxes and other dynamic analysis processes are prevalent in malware detection systems nowadays to enhance the capability of detecting 0-day malware. Therefore, techniques of anti-dynamic analysis (TADA) are prevalent in modern malware samples, and sandboxes can suffer from false negatives and analysis failures when... | {
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2411.05983 | Longitudinal Ensemble Integration for sequential classification with
multimodal data | [
"cs.LG",
"cs.AI"
] | Effectively modeling multimodal longitudinal data is a pressing need in various application areas, especially biomedicine. Despite this, few approaches exist in the literature for this problem, with most not adequately taking into account the multimodality of the data. In this study, we developed multiple configuration... | {
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2411.05985 | Emotional Images: Assessing Emotions in Images and Potential Biases in
Generative Models | [
"cs.CY",
"cs.CV"
] | This paper examines potential biases and inconsistencies in emotional evocation of images produced by generative artificial intelligence (AI) models and their potential bias toward negative emotions. In particular, we assess this bias by comparing the emotions evoked by an AI-produced image to the emotions evoked by pr... | {
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2411.05986 | Fine-Grained Reward Optimization for Machine Translation using Error
Severity Mappings | [
"cs.CL"
] | Reinforcement learning (RL) has been proven to be an effective and robust method for training neural machine translation systems, especially when paired with powerful reward models that accurately assess translation quality. However, most research has focused on RL methods that use sentence-level feedback, which leads ... | {
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2411.05987 | Multiuser Commitment over Noisy Channels | [
"cs.IT",
"cs.CR",
"math.IT"
] | We consider multi-user commitment models that capture the problem of enabling multiple bidders to simultaneously submit auctions to verifiers while ensuring that i) verifiers do not obtain information on the auctions until bidders reveal them at a later stage; and, ii) bidders cannot change their auction once committed... | {
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2411.05990 | Game-theoretic LLM: Agent Workflow for Negotiation Games | [
"cs.AI",
"cs.CL",
"cs.GT",
"cs.LG",
"cs.MA"
] | This paper investigates the rationality of large language models (LLMs) in strategic decision-making contexts, specifically within the framework of game theory. We evaluate several state-of-the-art LLMs across a spectrum of complete-information and incomplete-information games. Our findings reveal that LLMs frequently ... | {
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2411.05991 | GUIDEQ: Framework for Guided Questioning for progressive informational
collection and classification | [
"cs.CL"
] | Question Answering (QA) is an important part of tasks like text classification through information gathering. These are finding increasing use in sectors like healthcare, customer support, legal services, etc., to collect and classify responses into actionable categories. LLMs, although can support QA systems, they fac... | {
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2411.05993 | A Modular Conditional Diffusion Framework for Image Reconstruction | [
"cs.CV"
] | Diffusion Probabilistic Models (DPMs) have been recently utilized to deal with various blind image restoration (IR) tasks, where they have demonstrated outstanding performance in terms of perceptual quality. However, the task-specific nature of existing solutions and the excessive computational costs related to their t... | {
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2411.05994 | Modelling, design and control of middle-size tilt-rotor quadrotor | [
"eess.SY",
"cs.SY"
] | This paper explores the mathematical modelling and 3D design of a tilt-rotor quadrotor aircraft. The aircraft is a VTOL design and has capacity for one pilot. The design incorporates a part manual part automatic computerised flight control system and hybrid powertrain providing energy to eight ducted contrarotating pro... | {
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2411.05998 | Filling in Missing FX Implied Volatilities with Uncertainties: Improving
VAE-Based Volatility Imputation | [
"q-fin.ST",
"cs.LG",
"stat.ML"
] | Missing data is a common problem in finance and often requires methods to fill in the gaps, or in other words, imputation. In this work, we focused on the imputation of missing implied volatilities for FX options. Prior work has used variational autoencoders (VAEs), a neural network-based approach, to solve this proble... | {
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2411.05999 | Cyber-Physical Security of Vehicles: Zero Dynamics Attacks Against
Vehicle's Lateral Dynamics | [
"eess.SY",
"cs.SY"
] | Modern vehicles have evolved from mechanical systems to complex and connected ones controlled by numerous digital computers interconnected through internal networks. While this development has improved their efficiency and safety, it also brings new potential risks, particularly cyber-attacks. Several studies have expl... | {
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2411.06008 | The Dark Patterns of Personalized Persuasion in Large Language Models:
Exposing Persuasive Linguistic Features for Big Five Personality Traits in
LLMs Responses | [
"cs.CL",
"cs.AI"
] | This study explores how the Large Language Models (LLMs) adjust linguistic features to create personalized persuasive outputs. While research showed that LLMs personalize outputs, a gap remains in understanding the linguistic features of their persuasive capabilities. We identified 13 linguistic features crucial for in... | {
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2411.06009 | A Comprehensive Guide to Enhancing Antibiotic Discovery Using Machine
Learning Derived Bio-computation | [
"cs.AI"
] | Traditional drug discovery is a long, expensive, and complex process. Advances in Artificial Intelligence (AI) and Machine Learning (ML) are beginning to change this narrative. Here, we provide a comprehensive overview of different AI and ML tools that can be used to streamline and accelerate the drug discovery process... | {
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2411.06010 | Developing a Safety Management System for the Autonomous Vehicle
Industry | [
"cs.RO"
] | Safety Management Systems (SMSs) have been used in many safety-critical industries and are now being developed and deployed in the automated driving system (ADS)-equipped vehicle (AV) sector. Industries with decades of SMS deployment have established frameworks tailored to their specific context. Several frameworks for... | {
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2411.06011 | Exploring the Impact of Reflexivity Theory and Cognitive Social
Structures on the Dynamics of Doctor-Patient Social System | [
"cs.SI",
"cs.NE",
"physics.soc-ph"
] | Conventional economic and socio-behavioural models assume perfect symmetric access to information and rational behaviour among interacting agents in a social system. However, real-world events and observations appear to contradict such assumptions, leading to the possibility of other, more complex interaction rules exi... | {
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2411.06018 | A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time
Series via Visualization | [
"cs.LG",
"cs.AI"
] | Large language models (LLMs), with demonstrated reasoning abilities across multiple domains, are largely underexplored for time-series reasoning (TsR), which is ubiquitous in the real world. In this work, we propose TimerBed, the first comprehensive testbed for evaluating LLMs' TsR performance. Specifically, TimerBed i... | {
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2411.06019 | GaussianSpa: An "Optimizing-Sparsifying" Simplification Framework for
Compact and High-Quality 3D Gaussian Splatting | [
"cs.CV",
"cs.GR"
] | 3D Gaussian Splatting (3DGS) has emerged as a mainstream for novel view synthesis, leveraging continuous aggregations of Gaussian functions to model scene geometry. However, 3DGS suffers from substantial memory requirements to store the multitude of Gaussians, hindering its practicality. To address this challenge, we i... | {
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2411.06020 | Parallel Multi-path Feed Forward Neural Networks (PMFFNN) for Long
Columnar Datasets: A Novel Approach to Complexity Reduction | [
"cs.LG"
] | Traditional Feed-Forward Neural Networks (FFNN) and one-dimensional Convolutional Neural Networks (1D CNN) often encounter difficulties when dealing with long, columnar datasets that contain numerous features. The challenge arises from two primary factors: the large volume of data and the potential absence of meaningfu... | {
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2411.06022 | Improved intent classification based on context information using a
windows-based approach | [
"cs.CL"
] | Conversational systems have a Natural Language Understanding (NLU) module. In this module, there is a task known as an intent classification that aims at identifying what a user is attempting to achieve from an utterance. Previous works use only the current utterance to predict the intent of a given query and they do n... | {
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2411.06023 | Dynamic Textual Prompt For Rehearsal-free Lifelong Person
Re-identification | [
"cs.CV"
] | Lifelong person re-identification attempts to recognize people across cameras and integrate new knowledge from continuous data streams. Key challenges involve addressing catastrophic forgetting caused by parameter updating and domain shift, and maintaining performance in seen and unseen domains. Many previous works rel... | {
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2411.06032 | LLM-GLOBE: A Benchmark Evaluating the Cultural Values Embedded in LLM
Output | [
"cs.CL"
] | Immense effort has been dedicated to minimizing the presence of harmful or biased generative content and better aligning AI output to human intention; however, research investigating the cultural values of LLMs is still in very early stages. Cultural values underpin how societies operate, providing profound insights in... | {
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2411.06034 | CROPS: A Deployable Crop Management System Over All Possible State
Availabilities | [
"cs.AI"
] | Exploring the optimal management strategy for nitrogen and irrigation has a significant impact on crop yield, economic profit, and the environment. To tackle this optimization challenge, this paper introduces a deployable \textbf{CR}op Management system \textbf{O}ver all \textbf{P}ossible \textbf{S}tate availabilities ... | {
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2411.06037 | Sufficient Context: A New Lens on Retrieval Augmented Generation Systems | [
"cs.CL"
] | Augmenting LLMs with context leads to improved performance across many applications. Despite much research on Retrieval Augmented Generation (RAG) systems, an open question is whether errors arise because LLMs fail to utilize the context from retrieval or the context itself is insufficient to answer the query. To shed ... | {
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2411.06039 | To What Extent Does the Perceived Obesity Level of Humanoid Robots
Affect People's Trust in Them? | [
"cs.RO"
] | Despite obesity being widely discussed in the social sciences, the effect of a robot's perceived obesity level on trust is not covered by the field of HRI. While in research regarding humans, Body Mass Index (BMI) is commonly used as an indicator of obesity, this scale is completely irrelevant in the context of robots,... | {
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2411.06040 | CGLearn: Consistent Gradient-Based Learning for Out-of-Distribution
Generalization | [
"cs.LG",
"cs.AI"
] | Improving generalization and achieving highly predictive, robust machine learning models necessitates learning the underlying causal structure of the variables of interest. A prominent and effective method for this is learning invariant predictors across multiple environments. In this work, we introduce a simple yet po... | {
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2411.06041 | PointCG: Self-supervised Point Cloud Learning via Joint Completion and
Generation | [
"cs.CV",
"cs.AI"
] | The core of self-supervised point cloud learning lies in setting up appropriate pretext tasks, to construct a pre-training framework that enables the encoder to perceive 3D objects effectively. In this paper, we integrate two prevalent methods, masked point modeling (MPM) and 3D-to-2D generation, as pretext tasks withi... | {
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2411.06042 | Personalized Hierarchical Split Federated Learning in Wireless Networks | [
"cs.LG",
"cs.NI",
"cs.SY",
"eess.SY"
] | Extreme resource constraints make large-scale machine learning (ML) with distributed clients challenging in wireless networks. On the one hand, large-scale ML requires massive information exchange between clients and server(s). On the other hand, these clients have limited battery and computation powers that are often ... | {
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2411.06046 | Personalized News Recommendation System via LLM Embedding and
Co-Occurrence Patterns | [
"cs.AI"
] | In the past two years, large language models (LLMs) have achieved rapid development and demonstrated remarkable emerging capabilities. Concurrently, with powerful semantic understanding and reasoning capabilities, LLMs have significantly empowered the rapid advancement of the recommendation system field. Specifically, ... | {
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2411.06048 | An Empirical Analysis on Spatial Reasoning Capabilities of Large
Multimodal Models | [
"cs.CV",
"cs.AI"
] | Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks. However, their spatial reasoning capabilities are under-investigated. In this paper, we construct a novel VQA dataset, Spatial-MM, to comprehensively study LMMs' spatial understanding and reasoning capabilities.... | {
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2411.06055 | Linear Spherical Sliced Optimal Transport: A Fast Metric for Comparing
Spherical Data | [
"cs.LG",
"math.MG"
] | Efficient comparison of spherical probability distributions becomes important in fields such as computer vision, geosciences, and medicine. Sliced optimal transport distances, such as spherical and stereographic spherical sliced Wasserstein distances, have recently been developed to address this need. These methods red... | {
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2411.06056 | Learning Mixtures of Experts with EM | [
"cs.LG",
"stat.ML"
] | Mixtures of Experts (MoE) are Machine Learning models that involve partitioning the input space, with a separate "expert" model trained on each partition. Recently, MoE have become popular as components in today's large language models as a means to reduce training and inference costs. There, the partitioning function ... | {
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2411.06060 | Wild Narratives: Exploring the Effects of Animal Chatbots on Empathy and
Positive Attitudes toward Animals | [
"cs.HC",
"cs.AI"
] | Rises in the number of animal abuse cases are reported around the world. While chatbots have been effective in influencing their users' perceptions and behaviors, little if any research has hitherto explored the design of chatbots that embody animal identities for the purpose of eliciting empathy toward animals. We the... | {
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2411.06064 | Snippet-based Conversational Recommender System | [
"cs.IR"
] | Conversational Recommender Systems (CRS) engage users in interactive dialogues to gather preferences and provide personalized recommendations. Traditionally, CRS rely on pre-defined attributes or expensive, domain-specific annotated datasets to guide conversations, which limits flexibility and adaptability across domai... | {
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2411.06065 | DFT: A Dual-branch Framework of Fluctuation and Trend for Stock Price
Prediction | [
"cs.CE"
] | Stock price prediction is of significant importance in quantitative investment. Existing approaches encounter two primary issues: First, they often overlook the crucial role of capturing short-term stock fluctuations for predicting high-volatility returns. Second, mainstream methods, relying on graphs or attention mech... | {
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2411.06066 | Diversity and Inclusion in AI for Recruitment: Lessons from Industry
Workshop | [
"cs.AI"
] | Artificial Intelligence (AI) systems for online recruitment markets have the potential to significantly enhance the efficiency and effectiveness of job placements and even promote fairness or inclusive hiring practices. Neglecting Diversity and Inclusion (D&I) in these systems, however, can perpetuate biases, leading t... | {
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2411.06067 | AI-Driven Stylization of 3D Environments | [
"cs.CV",
"cs.GR"
] | In this system, we discuss methods to stylize a scene of 3D primitive objects into a higher fidelity 3D scene using novel 3D representations like NeRFs and 3D Gaussian Splatting. Our approach leverages existing image stylization systems and image-to-3D generative models to create a pipeline that iteratively stylizes an... | {
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2411.06068 | Zyda-2: a 5 Trillion Token High-Quality Dataset | [
"cs.CL",
"cs.AI"
] | In this technical report, we present Zyda-2: a five trillion token dataset for language model pretraining. Zyda-2 was used to train our Zamba2 series of models which are state-of-the-art for their weight class. We build Zyda-2 by collating high-quality open-source tokens such as FineWeb and DCLM, then distilling them t... | {
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2411.06069 | Model Selection for Average Reward RL with Application to Utility
Maximization in Repeated Games | [
"cs.LG",
"cs.GT",
"stat.ML"
] | In standard RL, a learner attempts to learn an optimal policy for a Markov Decision Process whose structure (e.g. state space) is known. In online model selection, a learner attempts to learn an optimal policy for an MDP knowing only that it belongs to one of $M >1$ model classes of varying complexity. Recent results h... | {
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2411.06070 | GFT: Graph Foundation Model with Transferable Tree Vocabulary | [
"cs.LG",
"cs.AI",
"cs.SI"
] | Inspired by the success of foundation models in applications such as ChatGPT, as graph data has been ubiquitous, one can envision the far-reaching impacts that can be brought by Graph Foundation Models (GFMs) with broader applications in the areas such as scientific research, social network analysis, drug discovery, an... | {
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2411.06071 | GlocalCLIP: Object-agnostic Global-Local Prompt Learning for Zero-shot
Anomaly Detection | [
"cs.CV"
] | Zero-shot anomaly detection (ZSAD) is crucial for detecting anomalous patterns in target datasets without using training samples, specifically in scenarios where there are distributional differences between the target domain and training data or where data scarcity arises because of restricted access. Although recently... | {
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2411.06074 | Aquila: A Hierarchically Aligned Visual-Language Model for Enhanced
Remote Sensing Image Comprehension | [
"cs.CV",
"cs.AI"
] | Recently, large vision language models (VLMs) have made significant strides in visual language capabilities through visual instruction tuning, showing great promise in the field of remote sensing image interpretation. However, existing remote sensing vision language models (RSVLMs) often fall short in capturing the com... | {
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2411.06076 | BreakGPT: Leveraging Large Language Models for Predicting Asset Price
Surges | [
"q-fin.ST",
"cs.LG"
] | This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. By leveraging both the capabilities of LLMs and Transformer-based models, this study evaluates BreakGPT and other Transformer-bas... | {
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2411.06078 | A Survey on Kolmogorov-Arnold Network | [
"cs.LG"
] | This systematic review explores the theoretical foundations, evolution, applications, and future potential of Kolmogorov-Arnold Networks (KAN), a neural network model inspired by the Kolmogorov-Arnold representation theorem. KANs distinguish themselves from traditional neural networks by using learnable, spline-paramet... | {
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2411.06084 | Optimizing Large Language Models through Quantization: A Comparative
Analysis of PTQ and QAT Techniques | [
"cs.LG",
"cs.AI",
"cs.CL"
] | This paper presents a comprehensive analysis of quantization techniques for optimizing Large Language Models (LLMs), specifically focusing on Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT). Through empirical evaluation across models ranging from 10M to 1B parameters, we demonstrate that quantiza... | {
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2411.06087 | Cross-Domain Transfer Learning using Attention Latent Features for
Multi-Agent Trajectory Prediction | [
"cs.AI",
"cs.RO"
] | With the advancements of sensor hardware, traffic infrastructure and deep learning architectures, trajectory prediction of vehicles has established a solid foundation in intelligent transportation systems. However, existing solutions are often tailored to specific traffic networks at particular time periods. Consequent... | {
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2411.06090 | Concept Bottleneck Language Models For protein design | [
"cs.LG"
] | We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our architecture offers three key benefits: i) Control: We can intervene on concept values to precisely control the properties of generated protein... | {
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2411.06091 | Pattern Integration and Enhancement Vision Transformer for
Self-Supervised Learning in Remote Sensing | [
"cs.CV"
] | Recent self-supervised learning (SSL) methods have demonstrated impressive results in learning visual representations from unlabeled remote sensing images. However, most remote sensing images predominantly consist of scenographic scenes containing multiple ground objects without explicit foreground targets, which limit... | {
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2411.06096 | ZhoBLiMP: a Systematic Assessment of Language Models with Linguistic
Minimal Pairs in Chinese | [
"cs.CL"
] | Whether and how language models (LMs) acquire the syntax of natural languages has been widely evaluated under the minimal pair paradigm. However, a lack of wide-coverage benchmarks in languages other than English has constrained systematic investigations into the issue. Addressing it, we first introduce ZhoBLiMP, the m... | {
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2411.06097 | A Multimodal Adaptive Graph-based Intelligent Classification Model for
Fake News | [
"cs.AI"
] | Numerous studies have been proposed to detect fake news focusing on multi-modalities based on machine and/or deep learning. However, studies focusing on graph-based structures using geometric deep learning are lacking. To address this challenge, we introduce the Multimodal Adaptive Graph-based Intelligent Classificatio... | {
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2411.06098 | An Architectural Approach to Enhance Deep Long-Tailed Learning | [
"cs.CV",
"cs.AI"
] | Deep long-tailed recognition has been widely studied to address the issue of imbalanced data distributions in real-world scenarios. However, there has been insufficient focus on the design of neural architectures, despite empirical evidence suggesting that architecture can significantly impact performance. In this pape... | {
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2411.06100 | Mutual-energy inner product optimization method for constructing feature
coordinates and image classification in Machine Learning | [
"cs.LG",
"stat.ML"
] | As a key task in machine learning, data classification is essentially to find a suitable coordinate system to represent data features of different classes of samples. This paper proposes the mutual-energy inner product optimization method for constructing a feature coordinate system. First, by analyzing the solution sp... | {
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2411.06101 | Detecting Reference Errors in Scientific Literature with Large Language
Models | [
"cs.CL"
] | Reference errors, such as citation and quotation errors, are common in scientific papers. Such errors can result in the propagation of inaccurate information, but are difficult and time-consuming to detect, posing a significant challenge to scientific publishing. To support automatic detection of reference errors, this... | {
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2411.06102 | SiriusBI: Building End-to-End Business Intelligence Enhanced by Large
Language Models | [
"cs.DB"
] | The rapid advancement of AI technologies, particularly Large Language Models (LLMs), is establishing a new paradigm for Business Intelligence (BI). Despite the emergence of pioneering work in enhancing BI systems with LLMs, we have identified the following three issues when deployed in real industrial scenarios: intera... | {
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2411.06106 | Personalize to generalize: Towards a universal medical multi-modality
generalization through personalization | [
"cs.CV",
"cs.AI"
] | The differences among medical imaging modalities, driven by distinct underlying principles, pose significant challenges for generalization in multi-modal medical tasks. Beyond modality gaps, individual variations, such as differences in organ size and metabolic rate, further impede a model's ability to generalize effec... | {
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2411.06107 | A capacity renting framework for shared energy storage considering
peer-to-peer energy trading of prosumers with privacy protection | [
"eess.SY",
"cs.SY"
] | Shared energy storage systems (ESS) present a promising solution to the temporal imbalance between energy generation from renewable distributed generators (DGs) and the power demands of prosumers. However, as DG penetration rates rise, spatial energy imbalances become increasingly significant, necessitating the integra... | {
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2411.06111 | Energy-efficient Hybrid Model Predictive Trajectory Planning for
Autonomous Electric Vehicles | [
"cs.RO",
"cs.AI"
] | To tackle the twin challenges of limited battery life and lengthy charging durations in electric vehicles (EVs), this paper introduces an Energy-efficient Hybrid Model Predictive Planner (EHMPP), which employs an energy-saving optimization strategy. EHMPP focuses on refining the design of the motion planner to be seaml... | {
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2411.06112 | Interpret the Internal States of Recommendation Model with Sparse
Autoencoder | [
"cs.IR"
] | Explainable recommendation systems are important to enhance transparency, accuracy, and fairness. Beyond result-level explanations, model-level interpretations can provide valuable insights that allow developers to optimize system designs and implement targeted improvements. However, most current approaches depend on s... | {
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2411.06113 | Behavior-Aware Efficient Detection of Malicious EVs in V2G Systems | [
"eess.SY",
"cs.SY"
] | With the rapid development of electric vehicles (EVs) and vehicle-to-grid (V2G) technology, detecting malicious EV drivers is becoming increasingly important for the reliability and efficiency of smart grids. To address this challenge, machine learning (ML) algorithms are employed to predict user behavior and identify ... | {
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2411.06116 | Supernotes: Driving Consensus in Crowd-Sourced Fact-Checking | [
"cs.SI"
] | X's Community Notes, a crowd-sourced fact-checking system, allows users to annotate potentially misleading posts. Notes rated as helpful by a diverse set of users are prominently displayed below the original post. While demonstrably effective at reducing misinformation's impact when notes are displayed, there is an opp... | {
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2411.06119 | Scalable, Tokenization-Free Diffusion Model Architectures with Efficient
Initial Convolution and Fixed-Size Reusable Structures for On-Device Image
Generation | [
"cs.CV",
"cs.LG"
] | Vision Transformers and U-Net architectures have been widely adopted in the implementation of Diffusion Models. However, each architecture presents specific challenges while realizing them on-device. Vision Transformers require positional embedding to maintain correspondence between the tokens processed by the transfor... | {
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2411.06120 | Evaluating the Propensity of Generative AI for Producing Harmful
Disinformation During an Election Cycle | [
"cs.AI"
] | Generative Artificial Intelligence offers a powerful tool for adversaries who wish to engage in influence operations, such as the Chinese Spamouflage operation and the Russian Internet Research Agency effort that both sought to interfere with recent US election cycles. Therefore, this study seeks to investigate the pro... | {
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2411.06121 | SniffySquad: Patchiness-Aware Gas Source Localization with Multi-Robot
Collaboration | [
"cs.RO",
"cs.MA"
] | Gas source localization is pivotal for the rapid mitigation of gas leakage disasters, where mobile robots emerge as a promising solution. However, existing methods predominantly schedule robots' movements based on reactive stimuli or simplified gas plume models. These approaches typically excel in idealized, simulated ... | {
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2411.06122 | Characteristics of Political Misinformation Over the Past Decade | [
"cs.SI",
"cs.AI"
] | Although misinformation tends to spread online, it can have serious real-world consequences. In order to develop automated tools to detect and mitigate the impact of misinformation, researchers must leverage algorithms that can adapt to the modality (text, images and video), the source, and the content of the false inf... | {
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2411.06124 | Exploring Structural Nonlinearity in Binary Polariton-Based Neuromorphic
Architectures | [
"cond-mat.dis-nn",
"cs.ET",
"cs.LG",
"cs.NE",
"physics.app-ph"
] | This study investigates the performance of a binarized neuromorphic network leveraging polariton dyads, optically excited pairs of interfering polariton condensates within a microcavity to function as binary logic gate neurons. Employing numerical simulations, we explore various neuron configurations, both linear (NAND... | {
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2411.06128 | Research on reinforcement learning based warehouse robot navigation
algorithm in complex warehouse layout | [
"cs.RO",
"cs.AI"
] | In this paper, how to efficiently find the optimal path in complex warehouse layout and make real-time decision is a key problem. This paper proposes a new method of Proximal Policy Optimization (PPO) and Dijkstra's algorithm, Proximal policy-Dijkstra (PP-D). PP-D method realizes efficient strategy learning and real-ti... | {
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2411.06135 | Online Parallel Multi-Task Relationship Learning via Alternating
Direction Method of Multipliers | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Online multi-task learning (OMTL) enhances streaming data processing by leveraging the inherent relations among multiple tasks. It can be described as an optimization problem in which a single loss function is defined for multiple tasks. Existing gradient-descent-based methods for this problem might suffer from gradien... | {
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} |
2411.06136 | Decentralized Semantic Communication and Cooperative Tracking Control
for a UAV Swarm over Wireless MIMO Fading Channels | [
"eess.SP",
"cs.SY",
"eess.SY"
] | This paper investigates the semantic communication and cooperative tracking control for an UAV swarm comprising a leader UAV and a group of follower UAVs, all interconnected via unreliable wireless multiple-input-multiple-output (MIMO) channels. Initially, we develop a dynamic model for the UAV swarm that accounts for ... | {
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2411.06138 | StopHC: A Harmful Content Detection and Mitigation Architecture for
Social Media Platforms | [
"cs.SI",
"cs.CL"
] | The mental health of social media users has started more and more to be put at risk by harmful, hateful, and offensive content. In this paper, we propose \textsc{StopHC}, a harmful content detection and mitigation architecture for social media platforms. Our aim with \textsc{StopHC} is to create more secure online envi... | {
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2411.06140 | Deep Nonparametric Conditional Independence Tests for Images | [
"stat.ML",
"cs.LG",
"eess.IV",
"math.ST",
"stat.ME",
"stat.TH"
] | Conditional independence tests (CITs) test for conditional dependence between random variables. As existing CITs are limited in their applicability to complex, high-dimensional variables such as images, we introduce deep nonparametric CITs (DNCITs). The DNCITs combine embedding maps, which extract feature representatio... | {
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2411.06142 | Aquila-plus: Prompt-Driven Visual-Language Models for Pixel-Level Remote
Sensing Image Understanding | [
"cs.CV",
"cs.AI"
] | The recent development of vision language models (VLMs) has led to significant advances in visual-language integration through visual instruction tuning, and they have rapidly evolved in the field of remote sensing image understanding, demonstrating their powerful capabilities. However, existing RSVLMs mainly focus on ... | {
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2411.06146 | AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for
AI Systems | [
"cs.AI"
] | AI systems, in particular with deep learning techniques, have demonstrated superior performance for various real-world applications. Given the need for tailored optimization in specific scenarios, as well as the concerns related to the exploits of subsurface vulnerabilities, a more comprehensive and in-depth testing AI... | {
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2411.06148 | Deep Reinforcement Learning for Digital Twin-Oriented Complex Networked
Systems | [
"cs.AI"
] | The Digital Twin Oriented Complex Networked System (DT-CNS) aims to build and extend a Complex Networked System (CNS) model with progressively increasing dynamics complexity towards an accurate reflection of reality -- a Digital Twin of reality. Our previous work proposed evolutionary DT-CNSs to model the long-term ada... | {
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2411.06151 | Building an Efficient Multilingual Non-Profit IR System for the Islamic
Domain Leveraging Multiprocessing Design in Rust | [
"cs.CL"
] | The widespread use of large language models (LLMs) has dramatically improved many applications of Natural Language Processing (NLP), including Information Retrieval (IR). However, domains that are not driven by commercial interest often lag behind in benefiting from AI-powered solutions. One such area is religious and ... | {
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2411.06155 | HiHa: Introducing Hierarchical Harmonic Decomposition to Implicit Neural
Compression for Atmospheric Data | [
"cs.LG",
"cs.IT",
"math.IT",
"physics.ao-ph"
] | The rapid development of large climate models has created the requirement of storing and transferring massive atmospheric data worldwide. Therefore, data compression is essential for meteorological research, but an efficient compression scheme capable of keeping high accuracy with high compressibility is still lacking.... | {
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} |
2411.06158 | Fast High-dimensional Approximate Nearest Neighbor Search with Efficient
Index Time and Space | [
"cs.DB"
] | Approximate K nearest neighbor (AKNN) search in high-dimensional Euclidean space is a fundamental problem with widespread applications. Vector quantization which maps vectors to discrete quantized code, can significantly reduce the space cost of AKNN search while also accelerating the AKNN search speed. The exclusive u... | {
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} |
2411.06159 | Mixture of Knowledge Minigraph Agents for Literature Review Generation | [
"cs.CL",
"cs.CE"
] | Literature reviews play a crucial role in scientific research for understanding the current state of research, identifying gaps, and guiding future studies on specific topics. However, the process of conducting a comprehensive literature review is yet time-consuming. This paper proposes a novel framework, collaborative... | {
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} |
2411.06160 | Expansion Quantization Network: An Efficient Micro-emotion Annotation
and Detection Framework | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.HC",
"cs.LG"
] | Text emotion detection constitutes a crucial foundation for advancing artificial intelligence from basic comprehension to the exploration of emotional reasoning. Most existing emotion detection datasets rely on manual annotations, which are associated with high costs, substantial subjectivity, and severe label imbalanc... | {
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} |
2411.06161 | A New 8/14 Two-Phase Switched Reluctance Motor with Improved Performance | [
"eess.SY",
"cs.SY"
] | Despite their simple and robust structure, low cost, and simple cooling system, switched reluctance motors (SRMs) face the challenge of low mean torque. A possible solution is to change the structure of SRMs. This article introduces an innovative combination of the number of rotor teeth and stator teeth of a two-phase ... | {
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} |
2411.06166 | Towards an Efficient Synthetic Image Data Pipeline for Training
Vision-Based Robot Systems | [
"cs.RO"
] | Training data is an essential resource for creating capable and robust vision systems which are integral to the proper function of many robotic systems. Synthesized training data has been shown in recent years to be a viable alternative to manually collecting and labelling data. In order to meet the rising popularity o... | {
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} |
2411.06171 | SEEKR: Selective Attention-Guided Knowledge Retention for Continual
Learning of Large Language Models | [
"cs.CL",
"cs.LG"
] | Continual learning (CL) is crucial for language models to dynamically adapt to the evolving real-world demands. To mitigate the catastrophic forgetting problem in CL, data replay has been proven a simple and effective strategy, and the subsequent data-replay-based distillation can further enhance the performance. Howev... | {
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} |
2411.06173 | LSSInst: Improving Geometric Modeling in LSS-Based BEV Perception with
Instance Representation | [
"cs.CV"
] | With the attention gained by camera-only 3D object detection in autonomous driving, methods based on Bird-Eye-View (BEV) representation especially derived from the forward view transformation paradigm, i.e., lift-splat-shoot (LSS), have recently seen significant progress. The BEV representation formulated by the frustu... | {
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} |
2411.06174 | State Chrono Representation for Enhancing Generalization in
Reinforcement Learning | [
"cs.LG",
"cs.RO"
] | In reinforcement learning with image-based inputs, it is crucial to establish a robust and generalizable state representation. Recent advancements in metric learning, such as deep bisimulation metric approaches, have shown promising results in learning structured low-dimensional representation space from pixel observat... | {
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} |
2411.06175 | Clustering Algorithms and RAG Enhancing Semi-Supervised Text
Classification with Large LLMs | [
"cs.CL",
"cs.LG"
] | This paper proposes a Clustering, Labeling, then Augmenting framework that significantly enhances performance in Semi-Supervised Text Classification (SSTC) tasks, effectively addressing the challenge of vast datasets with limited labeled examples. Unlike traditional SSTC approaches that rely on a predefined small set o... | {
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} |
2411.06176 | M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding
And A Retrieval-Aware Tuning Framework | [
"cs.CL"
] | The ability to understand and answer questions over documents can be useful in many business and practical applications. However, documents often contain lengthy and diverse multimodal contents such as texts, figures, and tables, which are very time-consuming for humans to read thoroughly. Hence, there is an urgent nee... | {
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} |
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