Tag: Multimodal Data
All the articles with the tag "Multimodal Data".
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Task-Core Memory Management and Consolidation for Long-term Continual Learning
This paper introduces Long-CL, a human memory-inspired framework for long-term continual learning, leveraging task-core memory management and selective sample consolidation to significantly outperform baselines by 7.4% and 6.5% AP on two novel benchmarks, MMLongCL-Bench and TextLongCL-Bench, while mitigating catastrophic forgetting.
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Exploring the Trade-Offs: Quantization Methods, Task Difficulty, and Model Size in Large Language Models From Edge to Giant
This paper comprehensively evaluates the impact of four quantization methods (GPTQ, AWQ, SmoothQuant, FP8) on instruction-tuned LLMs and SLMs from 1B to 405B parameters across 13 datasets, revealing that quantized models often outperform smaller baselines but struggle with instruction-following and hallucination detection, with FP8 showing robustness and task difficulty not always correlating with accuracy loss.
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Rethinking Memory in AI: Taxonomy, Operations, Topics, and Future Directions
本文通过提出AI记忆系统的分类(参数、上下文结构化和非结构化)和六种基本操作(整合、更新、索引、遗忘、检索、压缩),系统化地综述了长期记忆、长上下文、参数修改和多源记忆等研究主题,并展望了未来方向。
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Language Models are Universal Embedders
本文基于多语言解码器模型(如BLOOM)提出通用嵌入器构建方法,通过对比学习和参数高效微调实现跨语言、跨任务的高质量嵌入,实验表明其在多语言和多任务场景中具有显著潜力和泛化能力。
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MOOSComp: Improving Lightweight Long-Context Compressor via Mitigating Over-Smoothing and Incorporating Outlier Scores
本文提出MOOSComp方法,通过在训练中添加inter-class cosine similarity loss缓解over-smoothing问题,并在压缩中整合outlier分数保留关键token,显著提升了任务无关的长上下文压缩性能和泛化能力。