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English(EN) Beyond Tokenization: Direct Timestep Embedding and Contrastive Alignment for Time-Series Question Answering

新的CADE框架通过绕过分词来增强时间序列问答

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了CADE（对比式对齐与直接嵌入）框架，旨在通过解决传统分词方法的局限性来改进时间序列问答（TSQA）。CADE使用逐点线性编码器和MLP投影仪将每个时间步直接嵌入到LLM的嵌入空间中，保留了标准分词中丢失的关键幅度、尺度和趋势信息。该框架还包含一个监督对比损失，用于将时间序列嵌入与语言表示对齐。在Time-MQA基准上的实验表明，CADE在六项TSQA任务中持续提升性能，超越了现有的LLM基线。 AI

影响 这项研究为LLM处理时间序列数据提供了一种新颖的方法，有望提高需要分析顺序数值数据的任务的性能。

排序理由 该集群包含一篇详细介绍时间序列问答新颖框架的研究论文。

在 arXiv cs.AI 阅读 →

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新的CADE框架通过绕过分词来增强时间序列问答

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该集群包含一篇详细介绍时间序列问答新颖框架的研究论文。
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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yafeng Wu, Huu Hiep Nguyen, Thin Nguyen, Hung Le ·

    超越分词：时间序列问答的直接时间步嵌入与对比度对齐

    arXiv:2606.18986v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering. However, directly feeding raw numerical series…

  2. arXiv cs.AI TIER_1 English(EN) · Hung Le ·

    超越分词：时间序列问答的直接时间步嵌入与对比度对齐

    Recent advances in large language models (LLMs) have given rise to time-series question answering (TSQA), which formulates time-series analysis as natural-language question answering. However, directly feeding raw numerical series into LLMs suffers from a tokenization bottleneck:…

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