Relational Probing: LM-to-Graph Adaptation for Financial Prediction
For financial prediction tasks, this method enables end-to-end training of language models to produce structured relational graphs, improving performance while maintaining efficiency.
Relational Probing replaces the standard language model head with a relation head to directly induce a relational graph from hidden states, trained jointly with a downstream stock-trend prediction model. It achieves consistent performance improvements over a co-occurrence baseline using Qwen3 backbones (0.6B/1.7B/4B) at competitive inference cost.
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.