arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2602.20859cs.CL
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FinAnchor:用于金融预测的对齐多模型表示

FinAnchor: Aligned Multi-Model Representations for Financial Prediction

New Jersey Institute of Technology(新泽西理工学院);Georgia Institute of Technology(佐治亚理工学院);Chinese University of Hong Kong, Shenzhen(香港中文大学(深圳))

Zirui He, Huopu Zhang, Yanguang Liu, Sirui Wu, Mengnan Du

AI总结:

FinAnchor通过整合多个LLM的嵌入并对齐到锚定空间,提升金融预测的鲁棒性与性能。

摘要:

从长文档进行金融预测面临重大挑战,因为可操作信号通常稀疏且被噪声掩盖,且生成嵌入的最优LLM会随任务和时间变化而变化。在本文中,我们提出FinAnchor(财务锚定表示),一种轻量级框架,该框架在不微调底层模型的情况下整合多个LLM的嵌入。FinAnchor通过选择一个锚定嵌入空间,并学习线性映射将其他模型的表示对齐到该锚定空间,以解决特征空间的不兼容性。这些对齐的特征随后被聚合,形成统一的表示,用于下游预测。在多个金融NLP任务中,FinAnchor始终优于强大的单模型基线和标准集成方法,证明了对齐异质表示在稳健金融预测中的有效性。

英文摘要:

Financial prediction from long documents involves significant challenges, as actionable signals are often sparse and obscured by noise, and the optimal LLM for generating embeddings varies across tasks and time periods. In this paper, we propose FinAnchor(Financial Anchored Representations), a lightweight framework that integrates embeddings from multiple LLMs without fine-tuning the underlying models. FinAnchor addresses the incompatibility of feature spaces by selecting an anchor embedding space and learning linear mappings to align representations from other models into this anchor. These aligned features are then aggregated to form a unified representation for downstream prediction. Across multiple financial NLP tasks, FinAnchor consistently outperforms strong single-model baselines and standard ensemble methods, demonstrating the effectiveness of anchoring heterogeneous representations for robust financial prediction.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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