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Large language models for forecasting and anomaly detection: A systematic literature review
This systematic literature review comprehensively examines the application of Large
Language Models (LLMs) in forecasting and anomaly detection, highlighting the current …
Language Models (LLMs) in forecasting and anomaly detection, highlighting the current …
Large models for time series and spatio-temporal data: A survey and outlook
Temporal data, notably time series and spatio-temporal data, are prevalent in real-world
applications. They capture dynamic system measurements and are produced in vast …
applications. They capture dynamic system measurements and are produced in vast …
Db-gpt: Empowering database interactions with private large language models
The recent breakthroughs in large language models (LLMs) are positioned to transition
many areas of software. Database technologies particularly have an important entanglement …
many areas of software. Database technologies particularly have an important entanglement …
Intelligent agents with llm-based process automation
While intelligent virtual assistants like Siri, Alexa, and Google Assistant have become
ubiquitous in modern life, they still face limitations in their ability to follow multi-step …
ubiquitous in modern life, they still face limitations in their ability to follow multi-step …
A survey on medical large language models: Technology, application, trustworthiness, and future directions
Large language models (LLMs), such as GPT series models, have received substantial
attention due to their impressive capabilities for generating and understanding human-level …
attention due to their impressive capabilities for generating and understanding human-level …
Llmrg: Improving recommendations through large language model reasoning graphs
Recommendation systems aim to provide users with relevant suggestions, but often lack
interpretability and fail to capture higher-level semantic relationships between user …
interpretability and fail to capture higher-level semantic relationships between user …
TagRec: Temporal-Aware Graph Contrastive Learning with Theoretical Augmentation for Sequential Recommendation
Sequential recommendation systems aim to predict the future behaviors of users based on
their historical interactions. Despite the success of neural architectures like Transformer and …
their historical interactions. Despite the success of neural architectures like Transformer and …
Sora detector: A unified hallucination detection for large text-to-video models
The rapid advancement in text-to-video (T2V) generative models has enabled the synthesis
of high-fidelity video content guided by textual descriptions. Despite this significant progress …
of high-fidelity video content guided by textual descriptions. Despite this significant progress …
Llm-guided multi-view hypergraph learning for human-centric explainable recommendation
As personalized recommendation systems become vital in the age of information overload,
traditional methods relying solely on historical user interactions often fail to fully capture the …
traditional methods relying solely on historical user interactions often fail to fully capture the …
Data-centric financial large language models
Large language models (LLMs) show promise for natural language tasks but struggle when
applied directly to complex domains like finance. LLMs have difficulty reasoning about and …
applied directly to complex domains like finance. LLMs have difficulty reasoning about and …