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View article: Self-Monitoring Large Language Models for Click-Through Rate Prediction
Self-Monitoring Large Language Models for Click-Through Rate Prediction Open
Click-through rate prediction tasks estimate interaction probabilities using user–item features (i.e., the combined set of user and item features). LLMs have emerged as a promising approach by organizing these features into prompts and fin…
View article: Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning
Graph-MLLM: Harnessing Multimodal Large Language Models for Multimodal Graph Learning Open
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in representing and understanding diverse modalities. However, they typically focus on modality alignment in a pairwise manner while overlooking structural …
View article: Large language models for disease diagnosis: a scoping review
Large language models for disease diagnosis: a scoping review Open
Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs …
View article: FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets
FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets Open
Low-rank adaptation (LoRA) methods show great potential for scaling pre-trained general-purpose Large Language Models (LLMs) to hundreds or thousands of use scenarios. However, their efficacy in high-stakes domains like finance is rarely e…
View article: Beyond Pairwise Learning-To-Rank At Airbnb
Beyond Pairwise Learning-To-Rank At Airbnb Open
There are three fundamental asks from a ranking algorithm: it should scale to handle a large number of items, sort items accurately by their utility, and impose a total order on the items for logical consistency. But here's the catch-no al…
View article: Customized FinGPT Search Agents Using Foundation Models
Customized FinGPT Search Agents Using Foundation Models Open
Current large language models (LLMs) have proven useful for analyzing\nfinancial data, but most existing models, such as BloombergGPT and FinGPT, lack\ncustomization for specific user needs. In this paper, we address this gap by\ndevelopin…
View article: Large Language Models for Disease Diagnosis: A Scoping Review
Large Language Models for Disease Diagnosis: A Scoping Review Open
Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs …
View article: Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning Open
The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science. However, concerns regarding data privacy have emerged, …
View article: A-I-RAVEN and I-RAVEN-Mesh: Two New Benchmarks for Abstract Visual Reasoning
A-I-RAVEN and I-RAVEN-Mesh: Two New Benchmarks for Abstract Visual Reasoning Open
We study generalization and knowledge reuse capabilities of deep neural networks in the domain of abstract visual reasoning (AVR), employing Raven's Progressive Matrices (RPMs), a recognized benchmark task for assessing AVR abilities. Two …
View article: LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting
LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting Open
Time Series Forecasting (TSF) has long been a challenge in time series analysis. Inspired by the success of Large Language Models (LLMs), researchers are now developing Large Time Series Models (LTSMs)-universal transformer-based models th…
View article: GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models
GAugLLM: Improving Graph Contrastive Learning for Text-Attributed Graphs with Large Language Models Open
This work studies self-supervised graph learning for text-attributed graphs (TAGs) where nodes are represented by textual attributes. Unlike traditional graph contrastive methods that perturb the numerical feature space and alter the graph…
View article: GraphFM: A Comprehensive Benchmark for Graph Foundation Model
GraphFM: A Comprehensive Benchmark for Graph Foundation Model Open
Foundation Models (FMs) serve as a general class for the development of artificial intelligence systems, offering broad potential for generalization across a spectrum of downstream tasks. Despite extensive research into self-supervised lea…
View article: Denoising-Aware Contrastive Learning for Noisy Time Series
Denoising-Aware Contrastive Learning for Noisy Time Series Open
Time series self-supervised learning (SSL) aims to exploit unlabeled data for pre-training to mitigate the reliance on labels. Despite the great success in recent years, there is limited discussion on the potential noise in the time series…
View article: Cost-efficient Knowledge-based Question Answering with Large Language Models
Cost-efficient Knowledge-based Question Answering with Large Language Models Open
Knowledge-based question answering (KBQA) is widely used in many scenarios that necessitate domain knowledge. Large language models (LLMs) bring opportunities to KBQA, while their costs are significantly higher and absence of domain-specif…
View article: DCAI: Data-centric Artificial Intelligence
DCAI: Data-centric Artificial Intelligence Open
The emergence of Data-centric AI (DCAI) represents a pivotal shift in AI development, redirecting focus from model refinement to prioritizing data quality. This paradigmatic transition emphasizes the critical role of data in AI. While past…
View article: E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classification
E2GNN: Efficient Graph Neural Network Ensembles for Semi-Supervised Classification Open
This work studies ensemble learning for graph neural networks (GNNs) under the popular semi-supervised setting. Ensemble learning has shown superiority in improving the accuracy and robustness of traditional machine learning by combining t…
View article: Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering
Modality-Aware Integration with Large Language Models for Knowledge-based Visual Question Answering Open
Knowledge-based visual question answering (KVQA) has been extensively studied to answer visual questions with external knowledge, e.g., knowledge graphs (KGs). While several attempts have been proposed to leverage large language models (LL…
View article: Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning Open
The surge in interest and application of large language models (LLMs) has sparked a drive to fine-tune these models to suit specific applications, such as finance and medical science. However, concerns regarding data privacy have emerged, …
View article: KnowGPT: Knowledge Graph based Prompting for Large Language Models
KnowGPT: Knowledge Graph based Prompting for Large Language Models Open
Large Language Models (LLMs) have demonstrated remarkable capabilities in many real-world applications. Nonetheless, LLMs are often criticized for their tendency to produce hallucinations, wherein the models fabricate incorrect statements …
View article: Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards
Enhanced Generalization through Prioritization and Diversity in Self-Imitation Reinforcement Learning over Procedural Environments with Sparse Rewards Open
Exploration poses a fundamental challenge in Reinforcement Learning (RL) with sparse rewards, limiting an agent's ability to learn optimal decision-making due to a lack of informative feedback signals. Self-Imitation Learning (self-IL) has…
View article: Tackling Diverse Minorities in Imbalanced Classification
Tackling Diverse Minorities in Imbalanced Classification Open
Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers. When working with large datasets, the imbalanced issue can be further exacerbated, making it exception…
View article: DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research Open
The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-bas…
View article: DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research Open
The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research. Traditional keyword-bas…
View article: FinGPT: Democratizing Internet-scale Data for Financial Large Language Models
FinGPT: Democratizing Internet-scale Data for Financial Large Language Models Open
Large language models (LLMs) have demonstrated remarkable proficiency in understanding and generating human-like texts, which may potentially revolutionize the finance industry. However, existing LLMs often fall short in the financial fiel…
View article: Adaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering
Adaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering Open
The graph neural network-based collaborative filtering (CF) models user-item interactions as a bipartite graph and performs iterative aggregation to enhance performance. Unfortunately, the aggregation process may amplify the popularity bia…
View article: OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning Open
Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node c…
View article: Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model Open
With the rapid growth in model size, fine-tuning the large pre-trained language model has become increasingly difficult due to its extensive memory usage. Previous works usually focus on reducing the number of trainable parameters in the n…
View article: Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models Open
Sharding a large machine learning model across multiple devices to balance the costs is important in distributed training. This is challenging because partitioning is NP-hard, and estimating the costs accurately and efficiently is difficul…
View article: Dynamic Datasets and Market Environments for Financial Reinforcement Learning
Dynamic Datasets and Market Environments for Financial Reinforcement Learning Open
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environments for training financial reinforcement learning (FinRL) ag…