Expression Matters: Task Adaptation through Self-Generated Supervision

While the next-token prediction (NTP) paradigm enables large language models (LLMs) to be versatile for various tasks, the NTP interface is not naturally matched to non-generative tasks whose desired outputs are not token sequences. We argue that LLM performance can be limited not only by what models know, but also by how they express what they know. We refer to the latter as the knowledge expression bottleneck. We introduce Self-Knowledge Re-expression (SKR), a task-general method that relies on a model's self-generated signals to align its output mechanism with a task-native format. SKR requires only unlabeled task inputs and an explicit task configuration, without relying on any human-provided annotations or external teacher models' outputs. This makes SKR much more cost-efficient to implement and allows it to be executed locally without sending task data to any external annotation service. Experiments across multiple tasks on an industrial financial-document dataset demonstrate substantial benefits of SKR. It achieves an absolute Recall@1 gain of at least 40% on information retrieval, reduces latency by at least 76% for instruction-conditioned region localization, and achieves an absolute AUPRC gain of at least 33% on imbalanced binary classification. On MMDocRAG, the strongest SKR model exceeds the best published baseline by an absolute 16.9% in Recall@10. These results show that, in addition to knowledge acquisition, knowledge expression is an important bottleneck in LLM task performance and that SKR provides a cost-efficient method for task adaptation.