Paper-Conference

Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study
Retrieval-augmented generation (RAG) is increasingly recognized as an effective approach to mitigating the hallucination of large …
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TableTime: Reformulating Time Series Classification as Training-Free Table Understanding with Large Language Models
Large language models (LLMs) have shown promise in multivariate time series classification (MTSC). To effectively adapt LLMs for MTSC, …
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Tag-augmented Dual-target Cross-domain Recommendation
Cross-domain recommendation (CDR) has been proposed to alleviate the data sparsity issue in recommendation systems and has garnered …
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am-ELO: A Stable Framework for Arena-based LLM Evaluation
Arena-based evaluation is a fundamental yet significant evaluation paradigm for modern AI models, especially large language models …
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HoH: A Dynamic Benchmark for Evaluating the Impact of Outdated Information on Retrieval-Augmented Generation
While Retrieval-Augmented Generation (RAG) has emerged as an effective approach for addressing the knowledge outdating problem in Large …
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TimeDART: A Diffusion Autoregressive Transformer for Self-supervised Time Series Representation
Self-supervised learning has garnered increasing attention in time series analysis for benefiting various downstream tasks and reducing …
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ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis
Designing effective models for learning time series representations is foundational for time series analysis. Many previous works …
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Cross-Domain Pre-training with Language Models for Transferable Time Series Representations
Pre-training universal models across multiple domains to enhance downstream tasks is a prevalent learning paradigm. However, there has …
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InstrucTime: Advancing Time Series Classification with Multimodal Language Modeling
For the advancement of time series classification, we can summarize that most existing methods adopt a common learning-to-classify …
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Recognizing unseen objects via multimodal intensive knowledge graph propagation
Zero-Shot Learning (ZSL), which aims at automatically recognizing unseen objects, is a promising learning paradigm to understand new …
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