用关系探测取代传统提示，让语言模型直接生成金融实体图谱预测股价。
Relational Probing: LM-to-Graph Adaptation for Financial Prediction
- 用关系头从语言模型隐藏状态直接构建金融实体图谱。
- 在多个小模型上实现稳定提升，推理成本可控。
- 适合需要结构化金融知识的量化或风控场景。
语言模型可用于识别文本中的金融实体关系，但现有基于提示的管道仍需自回归解码，且图结构构建与下游优化分离。本文提出「关系探测」，将标准语言模型头替换为关系头，直接从语言模型隐藏状态生成关系图，并与下游股票趋势预测模型联合训练。该方法同时学习语义表示并保持生成图的严格结构，使语言模型输出超越文本，可重构为适配下游任务的格式。为提高可复现性，定义小型语言模型（SLMs）：可在单张24GB显卡上以指定批次大小和序列长度进行端到端微调的模型。实验使用Qwen3（0.6B/1.7B/4B）作为上游SLM，对比共现基线，关系探测在保持竞争性推理成本的同时取得一致性能提升。
原文摘要 · Abstract (English)
Language models can be used to identify relationships between financial entities in text. However, while structured output mechanisms exist, prompting-based pipelines still incur autoregressive decoding costs and decouple graph construction from downstream optimization. We propose \emph{Relational Probing}, which replaces the standard language-model head with a relation head that induces a relational graph directly from language-model hidden states and is trained jointly with the downstream task model for stock-trend prediction. This approach both learns semantic representations and preserves the strict structure of the induced relational graph. It enables language-model outputs to go beyond text, allowing them to be reshaped into task-specific formats for downstream models. To enhance reproducibility, we provide an operational definition of small language models (SLMs): models that can be fine-tuned end-to-end on a single 24GB GPU under specified batch-size and sequence-length settings. Experiments use Qwen3 backbones (0.6B/1.7B/4B) as upstream SLMs and compare against a co-occurrence baseline. Relational Probing yields consistent performance improvements at competitive inference cost.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。