Relational Probing: LM-to-Graph Adaptation for Financial Prediction

• URL: http://arxiv.org/abs/2604.10212v1
• Date: Sat, 11 Apr 2026 13:43:56 GMT
• Title: Relational Probing: LM-to-Graph Adaptation for Financial Prediction
• Abstract summary: Language models can be used to identify relationships between financial entities in text.<n>We propose emphRelational Probing, which replaces the standard languagemodel head with a relational graph.<n>It enables language-model outputs to go beyond text, allowing them to be reshaped into task-specific formats for downstream models.
• Score: 3.7582774908258294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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